Computerized method and system for automated determination of high quality digital content

ABSTRACT

Disclosed are systems and methods for improving interactions with and between computers in a content generating, hosting and/or providing system supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatic discovery of high quality digital content. According to embodiments, the present disclosure describes improved computer system and methods directed to analyzing raw image data, such as features and descriptors of images in order to identify a high quality image(s). Such images can be identified from a database of images, and such images can be identified in real-time, or near real-time during the capture of an image(s) by a camera.

This application includes material that is subject to copyrightprotection. The copyright owner has no objection to the facsimilereproduction by anyone of the patent disclosure, as it appears in thePatent and Trademark Office files or records, but otherwise reserves allcopyright rights whatsoever.

FIELD

The present disclosure relates generally to improving the performance ofcontent generating, providing and/or hosting computer systems and/orplatforms by modifying the capabilities and enabling non-nativefunctionality to such systems and/or platforms for automatic discoveryof high quality digital content.

SUMMARY

In general, the present disclosure provides systems and methods forautomatic determination of high quality digital content. According toembodiments, the present disclosure describes improved computer systemand methods directed to analyzing raw image data, such as features anddescriptors of images, in order to identify the high quality images froma set of images. As discussed in more detail below, such images can beidentified from a database of images, and such images can be identifiedin real-time, or near real-time during the capture of an image(s) by acamera.

According to some embodiments, the disclosed systems and methods enablethe discovery of particular artistic works and/or artistic creators ofsuch works from large scale user generate content (UGC) collections(e.g., Flickr®). According to some embodiments, the identification ofsuch high quality images can occur during the process of capturing animage, via a user's camera-enabled device. For example, the disclosedsystems and methods can identify when the image data in-focus in thecamera's lens corresponds to a high-quality image, whereby the user canbe alerted to the same fact or the picture can be automatically taken.In another non-limiting example, upon a user taking a series ofphotographs of a visually similar image (e.g., a burst of photographs),the disclosed systems and methods can determine which image is the bestimage from the set.

Therefore, according to some embodiments of the present disclosure, thedisclosed systems and methods enable the discovery and/or identificationof high-quality content objects (for example, images) not only based onthe features of the content, but also based on structured and/or rawareas within and associated with such content. This enables thedisclosed systems and methods to promote such content in a number ways.For example, such identified images can be featured in prominent areason a website, or funneled into content licensing programs. Thus, thedisclosed systems and methods can, for example: (1) effectuate thediscovery of new, high quality content which can then be recommended tousers; (2) improve the experience of talented new users (who typicallylack a large following, and face a “cold start” problem); and (3) allowcontent curators to discover talented new users.

It will be recognized from the disclosure herein that embodimentsprovide improvements to a number of technology areas, for example thoserelated to systems and processes that handle or process images for usersor business entities, and provide for improved user loyalty, improvedimage publishing, improved advertising opportunities, improved imagesearch results, and improved picture taking.

In accordance with one or more embodiments, a method is disclosed whichincludes analyzing, via a computing device, a collection of usergenerated content (UGC) images to identify a first image, the firstimage having associated social data indicating user interest in thefirst image below a social threshold; parsing, via the computing device,the first image to extract raw image data, the raw image data comprisingfeatures associated with content of the first image; identifying, viathe computing device, a set of second images from the UGC collection,the second set of images being high-quality images, the identifyingcomprising identifying the raw image data of each of the second images;comparing, via the computing device, the raw image data of the firstimage with the raw image data of the second images, the comparisoncomprising identifying a similarity between the raw image data of thefirst image and the raw image data of each second image in accordancewith a comparison threshold; determining, via the computing device,whether the first image is a high-quality image based on the comparison,the high-quality determination based on whether the similarity betweenthe raw image data of the first image and the raw image data of eachsecond image satisfies the comparison threshold; and communicating, viathe computing device, information associated with the first image to atleast one user when the first image is determined to be a high-qualityimage.

In accordance with one or more embodiments, a method is disclosed whichincludes capturing, via a computing device, a set of images, thecaptured images comprising raw image data generated from the capturing;parsing, via the computing device, the set of images to extract the rawimage data, the raw image data of each image comprising featuresassociated with content of each captured image; comparing, via thecomputing device, the raw image data of each image with raw image dataof a second image, the comparison comprising identifying a similaritybetween the raw image data of the images and the raw image data of thesecond image; determining, via the computing device, a quality of eachimage based on the comparison, the quality based on the identifiedsimilarity; identifying, via the computing device, an image from the setof images having a highest quality; and communicating, via the computingdevice, information associated with the identified image to a user ofthe computing device.

In accordance with one or more embodiments, a method is disclosed whichincludes determining, via the computing device, image data of an imageoptically sensed by a lens of a camera associated with the computingdevice; analyzing, via the computing device, the image data to identifyfeatures associated with content of the depicted image; comparing, viathe computing device, the image data with image data of a second image,the second image being a high-quality image, the comparison comprisingidentifying a similarity between the image data of the depicted imageand the second image data in accordance with a comparison threshold;determining, via the computing device, a quality of the depicted imagebased on the comparison, the quality based on the identified similarity;and automatically capturing, via the computing device, the image usingthe camera when the quality satisfies a quality threshold.

In accordance with one or more embodiments, a non-transitorycomputer-readable storage medium is provided, the computer-readablestorage medium tangibly storing thereon, or having tangibly encodedthereon, computer readable instructions that when executed cause atleast one processor to perform a method for automatic discovery of highquality digital content.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code (or program logic) executed by aprocessor(s) of a computing device to implement functionality inaccordance with one or more such embodiments is embodied in, by and/oron a non-transitory computer-readable medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of thedisclosure will be apparent from the following description ofembodiments as illustrated in the accompanying drawings, in whichreference characters refer to the same parts throughout the variousviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating principles of the disclosure:

FIG. 1 is a schematic diagram illustrating an example of a networkwithin which the systems and methods disclosed herein could beimplemented according to some embodiments of the present disclosure;

FIG. 2 depicts is a schematic diagram illustrating an example of clientdevice in accordance with some embodiments of the present disclosure;

FIG. 3 is a schematic block diagram illustrating components of anexemplary system in accordance with embodiments of the presentdisclosure;

FIGS. 4A-4C are flowcharts illustrating steps performed in accordancewith some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure;

FIGS. 6A-6B are flowcharts illustrating steps performed in accordancewith some embodiments of the present disclosure;

FIG. 7 is a flowchart illustrating steps performed in accordance withsome embodiments of the present disclosure; and

FIG. 8 is a block diagram illustrating architecture of an exemplaryhardware device in accordance with one or more embodiments of thepresent disclosure.

DESCRIPTION OF EMBODIMENTS

The present disclosure will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, certain example embodiments. Subjectmatter may, however, be embodied in a variety of different forms and,therefore, covered or claimed subject matter is intended to be construedas not being limited to any example embodiments set forth herein;example embodiments are provided merely to be illustrative. Likewise, areasonably broad scope for claimed or covered subject matter isintended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The present disclosure is described below with reference to blockdiagrams and operational illustrations of methods and devices. It isunderstood that each block of the block diagrams or operationalillustrations, and combinations of blocks in the block diagrams oroperational illustrations, can be implemented by means of analog ordigital hardware and computer program instructions. These computerprogram instructions can be provided to a processor of a general purposecomputer to alter its function as detailed herein, a special purposecomputer, ASIC, or other programmable data processing apparatus, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, implement thefunctions/acts specified in the block diagrams or operational block orblocks. In some alternate implementations, the functions/acts noted inthe blocks can occur out of the order noted in the operationalillustrations. For example, two blocks shown in succession can in factbe executed substantially concurrently or the blocks can sometimes beexecuted in the reverse order, depending upon the functionality/actsinvolved.

These computer program instructions can be provided to a processor of: ageneral purpose computer to alter its function to a special purpose; aspecial purpose computer; ASIC; or other programmable digital dataprocessing apparatus, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, implement the functions/acts specified in the block diagramsor operational block or blocks, thereby transforming their functionalityin accordance with embodiments herein.

For the purposes of this disclosure a computer readable medium (orcomputer-readable storage medium/media) stores computer data, which datacan include computer program code (or computer-executable instructions)that is executable by a computer, in machine readable form. By way ofexample, and not limitation, a computer readable medium may comprisecomputer readable storage media, for tangible or fixed storage of data,or communication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

For the purposes of this disclosure the term “server” should beunderstood to refer to a service point which provides processing,database, and communication facilities. By way of example, and notlimitation, the term “server” can refer to a single, physical processorwith associated communications and data storage and database facilities,or it can refer to a networked or clustered complex of processors andassociated network and storage devices, as well as operating softwareand one or more database systems and application software that supportthe services provided by the server. Servers may vary widely inconfiguration or capabilities, but generally a server may include one ormore central processing units and memory. A server may also include oneor more mass storage devices, one or more power supplies, one or morewired or wireless network interfaces, one or more input/outputinterfaces, or one or more operating systems, such as Windows Server,Mac OS X, Unix, Linux, FreeBSD, or the like.

For the purposes of this disclosure a “network” should be understood torefer to a network that may couple devices so that communications may beexchanged, such as between a server and a client device or other typesof devices, including between wireless devices coupled via a wirelessnetwork, for example. A network may also include mass storage, such asnetwork attached storage (NAS), a storage area network (SAN), or otherforms of computer or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, cellular or any combination thereof. Likewise,sub-networks, which may employ differing architectures or may becompliant or compatible with differing protocols, may interoperatewithin a larger network. Various types of devices may, for example, bemade available to provide an interoperable capability for differingarchitectures or protocols. As one illustrative example, a router mayprovide a link between otherwise separate and independent LANs.

A communication link or channel may include, for example, analogtelephone lines, such as a twisted wire pair, a coaxial cable, full orfractional digital lines including T1, T2, T3, or T4 type lines,Integrated Services Digital Networks (ISDNs), Digital Subscriber Lines(DSLs), wireless links including satellite links, or other communicationlinks or channels, such as may be known to those skilled in the art.Furthermore, a computing device or other related electronic devices maybe remotely coupled to a network, such as via a wired or wireless lineor link, for example.

For purposes of this disclosure, a “wireless network” should beunderstood to couple client devices with a network. A wireless networkmay employ stand-alone ad-hoc networks, mesh networks, Wireless LAN(WLAN) networks, cellular networks, or the like. A wireless network mayfurther include a system of terminals, gateways, routers, or the likecoupled by wireless radio links, or the like, which may move freely,randomly or organize themselves arbitrarily, such that network topologymay change, at times even rapidly.

A wireless network may further employ a plurality of network accesstechnologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, WirelessRouter (WR) mesh, or 2nd, 3rd, or 4th generation (2G, 3G, or 4G)cellular technology, or the like. Network access technologies may enablewide area coverage for devices, such as client devices with varyingdegrees of mobility, for example.

For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

A computing device may be capable of sending or receiving signals, suchas via a wired or wireless network, or may be capable of processing orstoring signals, such as in memory as physical memory states, and may,therefore, operate as a server. Thus, devices capable of operating as aserver may include, as examples, dedicated rack-mounted servers, desktopcomputers, laptop computers, set top boxes, integrated devices combiningvarious features, such as two or more features of the foregoing devices,or the like. Servers may vary widely in configuration or capabilities,but generally a server may include one or more central processing unitsand memory. A server may also include one or more mass storage devices,one or more power supplies, one or more wired or wireless networkinterfaces, one or more input/output interfaces, or one or moreoperating systems, such as Windows Server, Mac OS X, Unix, Linux,FreeBSD, or the like.

For purposes of this disclosure, a client (or consumer or user) devicemay include a computing device capable of sending or receiving signals,such as via a wired or a wireless network. A client device may, forexample, include a desktop computer or a portable device, such as acellular telephone, a smart phone, a display pager, a radio frequency(RF) device, an infrared (IR) device an Near Field Communication (NFC)device, a Personal Digital Assistant (PDA), a handheld computer, atablet computer, a phablet, a laptop computer, a set top box, a wearablecomputer, smart watch, an integrated or distributed device combiningvarious features, such as features of the forgoing devices, or the like.

A client device may vary in terms of capabilities or features. Claimedsubject matter is intended to cover a wide range of potentialvariations. For example, a smart phone, phablet or tablet may include anumeric keypad or a display of limited functionality, such as amonochrome liquid crystal display (LCD) for displaying text. Incontrast, however, as another example, a web-enabled client device mayinclude one or more physical or virtual keyboards, mass storage, one ormore accelerometers, one or more gyroscopes, global positioning system(GPS) or other location-identifying type capability, or a display with ahigh degree of functionality, such as a touch-sensitive color 2D or 3Ddisplay, for example.

A client device may include or may execute a variety of operatingsystems, including a personal computer operating system, such as aWindows, iOS or Linux, or a mobile operating system, such as iOS,Android, or Windows Mobile, or the like.

A client device may include or may execute a variety of possibleapplications, such as a client software application enablingcommunication with other devices, such as communicating one or moremessages, such as via email, for example Yahoo! ® Mail, short messageservice (SMS), or multimedia message service (MMS), for example Yahoo!Messenger®, including via a network, such as a social network,including, for example, Tumblr®, Facebook®, LinkedIn®, Twitter®,Flickr®, or Google+®, Instagram™, to provide only a few possibleexamples. A client device may also include or execute an application tocommunicate content, such as, for example, textual content, multimediacontent, or the like. A client device may also include or execute anapplication to perform a variety of possible tasks, such as browsing,searching, playing various forms of content, including locally stored orstreamed video, or games (such as fantasy sports leagues). The foregoingis provided to illustrate that claimed subject matter is intended toinclude a wide range of possible features or capabilities.

The principles described herein may be embodied in many different forms.By way of background, the size of digital image archives in recent yearshas been growing exponentially. The wealth of images on the Internet isever-increasing due to archives of private users, marketers, contentfrom news publishing agencies, internet communities, and the like. Inline with the size of the archives, the difficulty of discovering newdigital content has become increasingly difficult and costly.

Conventional systems for identifying new, interesting content have onlyachieved limited effectiveness due to weaknesses in the implementedschemes. For example, conventional systems are mainly based on low-levelor hand-crafted features, which can only analyze very limited low-leveldescriptions about content and often fail to comprehensively identifynew content, as they are focused on existing, trending, high-ratedcontent. Additionally, such systems do not address how to identifycontent from a “cold start.” That is, new content that does not have theapplicable social indicators and/or identifiers indicating the content'squality and/or virality (e.g., user engagement within a community) arevirtually undiscoverable through conventional systems.

The disclosed systems and methods remedy such shortcomings in the artand provide improved computer systems and methods instituting increasedfunctionality to the computer executing the disclosed systems andmethods by automatically identifying high-quality content. As discussedbelow, the identification of high-quality content can yield improvementsin numerous technological fields, such as for example image search,content promotion and recommendation, image monetization, admonetization, and/or content selection from a set of captured imagery,to name a few.

The disclosed systems and methods enable a content generating, hostingand/or delivery system/platform to overcome specific technicalimpediments, such as for example “cold starts” (i.e., newly createdand/or uploaded content being undiscovered by users on a network due tothe lack of viewership, followers and/or social interaction, and thelike). For example, when content has had sufficient social interactions(e.g. views, likes, comments, shares, and the like), conventionalsystems can infer the quality of the content, and by proxy, theidentifying information of the artist (or content creator). As discussedabove, existing systems are generally devoid of any mechanism fordiscovering newly uploaded and/or created “cold” content (e.g., contentthat does not have the applicable social indicators and/or identifiersindicating the content's quality and/or user engagement/interest).

The disclosed systems and methods remedy these and other shortcomings byidentifying new content and the artists/creators associated with suchcontent based on the raw properties (i.e., features and descriptors) ofsuch content. Such raw data analysis, as discussed in more detail below,is not performed or contemplated by existing systems. The disclosedsystems and methods thereby enable the promotion of the identifiedcontent and/or artists which can lead to an identified artist being ableto accumulate followers. As evidenced from the discussion herein, byengaging users with appropriate followers early on (e.g., upon joining anetwork or posting new content), the instant disclosure's discussedmechanisms for content discovery can assist in boosting users' retentionwithin the social landscape of the Internet.

As discussed herein, the disclosed embodiments of systems and methodsare directed to solving the technical challenge of identifying newhigh-quality content (e.g., images). For purposes of this disclosure,“new content” refers to content (e.g., images, text, video, audio,multi-media, RSS feeds, and the like) that has been recently created,uploaded, downloaded, or shared, or even re-blogged/re-posted, such thatthe presence of such content on a user's account page (e.g., Flickr®page) is a recent occurrence. Those of skill in the art will understandthat “new” or “recent” refers to content's presence satisfying a recencythreshold. For example, if the recency threshold is one week, thecontent posted to the user's Flickr® page during that week would qualifyas “new content.” In some embodiments, “new content” can also refer tocontent that does not have the requisite social indicators (e.g., athreshold amount of followers or viewership), as discussed above.

As understood by those of skill in the art, the term “high-quality”refers to an item of digital content satisfying a quality threshold,which can be set by a user, site administrator, artistcreating/capturing the content, the system, or some combination thereof.In a non-limiting example, “high-quality” can refer to the digitalcontent being of interest to a user(s), where interest (or userengagement) can be based on the number of times a user has interactedwith the content (e.g., viewed, shared, commented, downloaded,re-blogged, re-posted, favorited, liked, and the like) at or above thequality threshold. In another non-limiting example, “high-quality”content can relay that the content is aesthetically pleasing ortechnically sound, in that the data associated with the content producesa resolution, focus, pixel quality, size, dimension, color scheme,exposure, white balance and the like, or some combination thereof thatsatisfies the quality threshold.

According to some embodiments, the disclosed systems and methods involvediscovering new, high-quality artistic works and/or informationassociated with the artistic creators responsible for such works fromlarge-scale user generated content (UGC) collections, such as, forexample, Flickr®. As discussed in more detail below, the systems andmethods discussed herein implement a machine learning quality model(referred to as the quality engine 300 below in relation to FIGS. 3-6B)(e.g., deep learning, in some embodiments) to an entire UGC collection.This effectuates a content quality model that can be built, maintainedand updated in a purely data driven manner, reliant upon the raw data ofthe content being hosted by the UGC collection.

According to some embodiments, once such artistic works (e.g., images)have been discovered from a UGC, the disclosed systems and methods canthen promote such content in a number of ways. For example, newlycreated and/or uploaded images can be featured in prominent areas on awebsite, made available for search or purchase/licensing, or funneledinto content licensing programs. Such promotion is based on the rawproperties of the content itself, whereby the social indicators haveonly an indirect influence on content's promotion. The disclosed systemsand methods can, for example: (1) effectuate the discovery of new, highquality content which can then be recommended to users; (2) improve theexperience of talented new users; and (3) allow content curators todiscover talented new users, (4) and provide increased monetizationopportunities.

For purposes of this disclosure, the identification of content will bein large measure directed to analyzing and discovering digital images;however, it should not be construed as limiting the scope of the instantapplication to solely images, as any known or to be known type ofcontent or media (e.g., text, video, audio, multi-media, RSS feeds, andthe like) is applicable to the disclosed systems and methods discussedherein.

According to some embodiments, as mentioned above, the disclosed systemsand methods can also enable the identification of high-quality imagesduring, prior and/or immediately after capturing an image. That is, thetrained quality model mentioned above, and discussed in more detailbelow, provides additional, non-native functionality to a device havingpicture taking capabilities to perform an aesthetics awarenessdetermination to assist in producing high-quality images. According tosome embodiments, the functionality discussed herein can be provided asa downloadable application that executes in conjunction with thehardware and software associated with a device's camera or can beimplemented in a camera or image capture device. As used herein, theterm camera is broadly intended to mean any device capable of capturingimage data, whether in the optical, infrared or electromagneticspectrum. In some embodiments, the functionally discussed herein can beimplemented as added functionality to the operating system and/orsoftware of the device taking the picture.

According to some embodiments, the disclosed systems and methods enablethe selection of the best photograph(s) from a series of takenphotographs. In some embodiments, the disclosed systems and methods caneffectuate auto-focusing of a camera's lens or auto-filtering of theimage data “in focus” to capture the best picture based on thelearned/trained quality modeling discussed above. That is, the disclosedsystems and methods can perform real-time, on-the-spot analysis of theimage data that is in-focus in a camera's image capture components todetermine when the best possible picture is possible to be captured(which, in some embodiments, can be attributable to the camera'scapabilities, underlying hardware and/or executing software, camera'ssettings and current imaging environment). As discussed in more detailbelow, the analysis of the image data captured or in-focus, includingthe raw data (e.g., features and descriptors) of the image, can involvedeterminations of, for example, resolution, pixel quality, size,dimension, color scheme, exposure, white balance and the like.

For purposes of this discussion, the term “capture” is intended to meanthe process by which a device obtains a digital image, for examplethrough use of a camera's vision/image/thermal/IR/sonar or other sensor(e.g., lens and CCD combination and the like). The sensor can identify aframe (for a still photograph) or frames of imagery (for video frames)and convert the sensed information into digital values (the digitalvalues are referred to as image data below). These digital values can befed to a volatile or non-volatile memory associated with the cameradevice. Thus, as one of skill in the art would understand from thediscussion below, the instant disclosure involves embodiments where 1) acamera captures an image (i.e., actually takes and stores or saves thepicture—FIG. 6A); and, 2) where the image data being read by the visionsensor is analyzed prior to actually “taking” the picture (e.g., priorto capture during or after viewing or focusing or when in focus—FIG.6B).

According to some embodiments, the analysis of captured or focused(e.g., in focus in the camera lens or image capture component) image canbe utilized to further train the quality model. That is, in someembodiments, the determinations of the features and descriptors of theimage data can be fed back to the quality model using a recursive orbootstrapping function in order to continually update, or personalizethe aesthetic determinations, styles, data and data types. In someembodiments, such analysis can also be utilized to optimize parametersof an image filter, as commonly used on Flickr®, Instagram™ and thelike, where optimized filters can be implemented per site, or per user.Such optimization can automatically occur prior to taking the picture,during image capture, or immediately after image capture therebyensuring an image is a high quality image. In some embodiments, suchoptimization can be effectuated on the client-side application, or onthe server-side upon uploading the picture to the website associatedwith the application being used to capture the photograph.

As such, the disclosed systems and method involve embodiments foridentifying new, high-quality images from 1) a UGC collection of images(e.g., Flickr®) and/or 2) from pictures taken or in-focus on a camera.As discussed in more detail in relation to FIGS. 3-6B below, thedisclosed systems and methods can analyze image data (e.g., features anddescriptors such as, but not limited to image pixels) to produce anaesthetic score for an image. In some embodiments, this score, which ispart of the quality modeling of a UGC collection, as mentioned above anddiscussed below in more detail, can be utilized to identify the qualityof determined new images, whereby determined high-quality images can bepromoted to other users within the online community (e.g., withinFlickr®) or across network platforms (e.g., posting a message onTwitter® about the Flickr® image). In some embodiments, the scoring ofUGC images can be utilized as a basis for selecting the best photographfrom a series of photographs taken by a camera, or for capturing (oralert a user to) a high-quality image that is in-focus in the camera'slens.

As discussed in more detail below, according to some embodiments,information associated with or derived from the determined or discovered(e.g., previously uncovered) content, as discussed herein, can be usedfor monetization purposes and targeted advertising when providing,delivering or enabling access to such content. That is, providingtargeted advertising to users associated with such discovered contentcan lead to an increased click-through rate (CTR) of such ads and/or anincrease in the advertiser's return on investment (ROI) for serving suchcontent provided by third parties (e.g., advertisement content providedby an advertiser, where the advertiser can be a third party advertiser,or an entity directly associated with or hosting the systems and methodsdiscussed herein).

Certain embodiments will now be described in greater detail withreference to the figures. In general, with reference to FIG. 1, a system100 in accordance with an embodiment of the present disclosure is shown.FIG. 1 shows components of a general environment in which the systemsand methods discussed herein may be practiced. Not all the componentsmay be required to practice the disclosure, and variations in thearrangement and type of the components may be made without departingfrom the spirit or scope of the disclosure. As shown, system 100 of FIG.1 includes local area networks (“LANs”)/wide area networks(“WANs”)—network 105, wireless network 110, mobile devices (clientdevices) 102 and client device 101. FIG. 1 additionally includes avariety of servers, such as content server 106, application (or “App”)server 108, search server 120 and advertising (“ad”) server 130.

One embodiment of mobile devices 102 is described in more detail below.Generally, however, mobile devices 102 may include virtually anyportable computing device capable of receiving and sending a messageover a network, such as network 105, wireless network 110, or the like.Mobile devices 102 may also be described generally as client devicesthat are configured to be portable. Thus, mobile devices 102 may includevirtually any portable computing device capable of connecting to anothercomputing device and receiving information. Such devices includemulti-touch and portable devices such as, cellular telephones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR)devices, Personal Digital Assistants (PDAs), handheld computers, laptopcomputers, wearable computers, smart watch, tablet computers, phablets,integrated devices combining one or more of the preceding devices, andthe like. As such, mobile devices 102 typically range widely in terms ofcapabilities and features. For example, a cell phone may have a numerickeypad and a few lines of monochrome LCD display on which only text maybe displayed. In another example, a web-enabled mobile device may have atouch sensitive screen, a stylus, and an HD display in which both textand graphics may be displayed.

A web-enabled mobile device may include a browser application that isconfigured to receive and to send web pages, web-based messages, and thelike. The browser application may be configured to receive and displaygraphics, text, multimedia, and the like, employing virtually any webbased language, including a wireless application protocol messages(WAP), and the like. In one embodiment, the browser application isenabled to employ Handheld Device Markup Language (HDML), WirelessMarkup Language (WML), WMLScript, JavaScript, Standard GeneralizedMarkup Language (SMGL), HyperText Markup Language (HTML), eXtensibleMarkup Language (XML), and the like, to display and send a message.

Mobile devices 102 also may include at least one client application thatis configured to receive content from another computing device. Theclient application may include a capability to provide and receivetextual content, graphical content, audio content, and the like. Theclient application may further provide information that identifiesitself, including a type, capability, name, and the like. In oneembodiment, mobile devices 102 may uniquely identify themselves throughany of a variety of mechanisms, including a phone number, MobileIdentification Number (MIN), an electronic serial number (ESN), or othermobile device identifier.

In some embodiments, mobile devices 102 may also communicate withnon-mobile client devices, such as client device 101, or the like. Inone embodiment, such communications may include sending and/or receivingmessages, searching for and/or sharing photographs, audio clips, videoclips, or any of a variety of other forms of communications. Clientdevice 101 may include virtually any computing device capable ofcommunicating over a network to send and receive information. The set ofsuch devices may include devices that typically connect using a wired orwireless communications medium such as personal computers,multiprocessor systems, microprocessor-based or programmable consumerelectronics, network PCs, or the like. Thus, client device 101 may alsohave differing capabilities for displaying navigable views ofinformation.

Client devices 101-102 computing device may be capable of sending orreceiving signals, such as via a wired or wireless network, or may becapable of processing or storing signals, such as in memory as physicalmemory states, and may, therefore, operate as a server. Thus, devicescapable of operating as a server may include, as examples, dedicatedrack-mounted servers, desktop computers, laptop computers, set topboxes, integrated devices combining various features, such as two ormore features of the foregoing devices, or the like.

Wireless network 110 is configured to couple mobile devices 102 and itscomponents with network 105. Wireless network 110 may include any of avariety of wireless sub-networks that may further overlay stand-alonead-hoc networks, and the like, to provide an infrastructure-orientedconnection for mobile devices 102. Such sub-networks may include meshnetworks, Wireless LAN (WLAN) networks, cellular networks, and the like.

Network 105 is configured to couple content server 106, applicationserver 108, or the like, with other computing devices, including, clientdevice 101, and through wireless network 110 to mobile devices 102.Network 105 is enabled to employ any form of computer readable media forcommunicating information from one electronic device to another. Also,network 105 can include the Internet in addition to local area networks(LANs), wide area networks (WANs), direct connections, such as through auniversal serial bus (USB) port, other forms of computer-readable media,or any combination thereof. On an interconnected set of LANs, includingthose based on differing architectures and protocols, a router acts as alink between LANs, enabling messages to be sent from one to another,and/or other computing devices.

Within the communications networks utilized or understood to beapplicable to the present disclosure, such networks will employ variousprotocols that are used for communication over the network. Signalpackets communicated via a network, such as a network of participatingdigital communication networks, may be compatible with or compliant withone or more protocols. Signaling formats or protocols employed mayinclude, for example, TCP/IP, UDP, QUIC (Quick UDP Internet Connection),DECnet, NetBEUI, IPX, APPLETALK™, or the like. Versions of the InternetProtocol (IP) may include IPv4 or IPv6. The Internet refers to adecentralized global network of networks. The Internet includes localarea networks (LANs), wide area networks (WANs), wireless networks, orlong haul public networks that, for example, allow signal packets to becommunicated between LANs. Signal packets may be communicated betweennodes of a network, such as, for example, to one or more sites employinga local network address. A signal packet may, for example, becommunicated over the Internet from a user site via an access nodecoupled to the Internet. Likewise, a signal packet may be forwarded vianetwork nodes to a target site coupled to the network via a networkaccess node, for example. A signal packet communicated via the Internetmay, for example, be routed via a path of gateways, servers, etc. thatmay route the signal packet in accordance with a target address andavailability of a network path to the target address.

According to some embodiments, the present disclosure may also beutilized within or accessible to an electronic social networking site. Asocial network refers generally to an electronic network of individuals,such as acquaintances, friends, family, colleagues, or co-workers,coupled via a communications network or via a variety of sub-networks.Potentially, additional relationships may subsequently be formed as aresult of social interaction via the communications network orsub-networks. In some embodiments, multi-modal communications may occurbetween members of the social network. Individuals within one or moresocial networks may interact or communication with other members of asocial network via a variety of devices. Multi-modal communicationtechnologies refers to a set of technologies that permit interoperablecommunication across multiple devices or platforms, such as cell phones,smart phones, tablet computing devices, phablets, personal computers,televisions, set-top boxes, SMS/MMS, email, instant messenger clients,forums, social networking sites, or the like.

In some embodiments, the disclosed networks 110 and/or 105 may comprisea content distribution network(s). A “content delivery network” or“content distribution network” (CDN) generally refers to a distributedcontent delivery system that comprises a collection of computers orcomputing devices linked by a network or networks. A CDN may employsoftware, systems, protocols or techniques to facilitate variousservices, such as storage, caching, communication of content, orstreaming media or applications. A CDN may also enable an entity tooperate or manage another's site infrastructure, in whole or in part.

The content server 106 may include a device that includes aconfiguration to provide content via a network to another device. Acontent server 106 may, for example, host a site, such as an emailplatform or social networking site, or a personal user site (such as ablog, vlog, online dating site, and the like). A content server 106 mayalso host a variety of other sites, including, but not limited tobusiness sites, educational sites, dictionary sites, encyclopedia sites,wikis, financial sites, government sites, and the like. Devices that mayoperate as content server 106 include personal computers desktopcomputers, multiprocessor systems, microprocessor-based or programmableconsumer electronics, network PCs, servers, and the like.

Content server 106 can further provide a variety of services thatinclude, but are not limited to, search services, email services, photoservices, web services, social networking services, news services,third-party services, audio services, video services, instant messaging(IM) services, SMS services, MMS services, FTP services, voice over IP(VOIP) services, or the like. Such services, for example a search engineand/or search platform, can be provided via the search server 120,whereby a user is able to utilize such service upon the user beingauthenticated, verified or identified by the service. Examples ofcontent may include images, text, audio, video, or the like, which maybe processed in the form of physical signals, such as electricalsignals, for example, or may be stored in memory, as physical states,for example.

An ad server 130 comprises a server that stores online advertisementsfor presentation to users. “Ad serving” refers to methods used to placeonline advertisements on websites, in applications, or other placeswhere users are more likely to see them, such as during an onlinesession or during computing platform use, for example. Variousmonetization techniques or models may be used in connection withsponsored advertising, including advertising associated with user. Suchsponsored advertising includes monetization techniques includingsponsored search advertising, non-sponsored search advertising,guaranteed and non-guaranteed delivery advertising, adnetworks/exchanges, ad targeting, ad serving and ad analytics. Suchsystems can incorporate near instantaneous auctions of ad placementopportunities during web page creation, (in some cases in less than 500milliseconds) with higher quality ad placement opportunities resultingin higher revenues per ad. That is advertisers will pay higheradvertising rates when they believe their ads are being placed in oralong with highly relevant content that is being presented to users.Reductions in the time needed to quantify a high quality ad placementoffers ad platforms competitive advantages. Thus higher speeds and morerelevant context detection improve these technological fields.

For example, a process of buying or selling online advertisements mayinvolve a number of different entities, including advertisers,publishers, agencies, networks, or developers. To simplify this process,organization systems called “ad exchanges” may associate advertisers orpublishers, such as via a platform to facilitate buying or selling ofonline advertisement inventory from multiple ad networks. “Ad networks”refers to aggregation of ad space supply from publishers, such as forprovision en masse to advertisers. For web portals like Yahoo! ®,advertisements may be displayed on web pages or in apps resulting from auser-defined search based at least in part upon one or more searchterms. Advertising may be beneficial to users, advertisers or webportals if displayed advertisements are relevant to interests of one ormore users. Thus, a variety of techniques have been developed to inferuser interest, user intent or to subsequently target relevantadvertising to users. One approach to presenting targeted advertisementsincludes employing demographic characteristics (e.g., age, income, sex,occupation, etc.) for predicting user behavior, such as by group.Advertisements may be presented to users in a targeted audience based atleast in part upon predicted user behavior(s).

Another approach includes profile-type ad targeting. In this approach,user profiles specific to a user may be generated to model userbehavior, for example, by tracking a user's path through a web site ornetwork of sites, and compiling a profile based at least in part onpages or advertisements ultimately delivered. A correlation may beidentified, such as for user purchases, for example. An identifiedcorrelation may be used to target potential purchasers by targetingcontent or advertisements to particular users. During presentation ofadvertisements, a presentation system may collect descriptive contentabout types of advertisements presented to users. A broad range ofdescriptive content may be gathered, including content specific to anadvertising presentation system. Advertising analytics gathered may betransmitted to locations remote to an advertising presentation systemfor storage or for further evaluation. Where advertising analyticstransmittal is not immediately available, gathered advertising analyticsmay be stored by an advertising presentation system until transmittal ofthose advertising analytics becomes available.

Servers 106, 108, 120 and 130 may be capable of sending or receivingsignals, such as via a wired or wireless network, or may be capable ofprocessing or storing signals, such as in memory as physical memorystates. Devices capable of operating as a server may include, asexamples, dedicated rack-mounted servers, desktop computers, laptopcomputers, set top boxes, integrated devices combining various features,such as two or more features of the foregoing devices, or the like.Servers may vary widely in configuration or capabilities, but generally,a server may include one or more central processing units and memory. Aserver may also include one or more mass storage devices, one or morepower supplies, one or more wired or wireless network interfaces, one ormore input/output interfaces, or one or more operating systems, such asWindows Server, Mac OS X, Unix, Linux, FreeBSD, or the like.

In some embodiments, users are able to access services provided byservers 106, 108, 120 and/or 130. This may include in a non-limitingexample, authentication servers, search servers, email servers, socialnetworking services servers, SMS servers, IM servers, MMS servers,exchange servers, photo-sharing services servers, and travel servicesservers, via the network 105 using their various devices 101-102. Insome embodiments, applications, such as a blog, photo storage/sharingapplication or social networking application (e.g., Flickr®, Tumblr®,and the like), can be hosted by the application server 108 (or contentserver 106, search server 120 and the like). Thus, the applicationserver 108 can store various types of applications and applicationrelated information including application data and user profileinformation (e.g., identifying and behavioral information associatedwith a user). It should also be understood that content server 106 canalso store various types of data related to the content and servicesprovided by content server 106 in an associated content database 107, asdiscussed in more detail below. Embodiments exist where the network 105is also coupled with/connected to a Trusted Search Server (TSS) whichcan be utilized to render content in accordance with the embodimentsdiscussed herein.

Moreover, although FIG. 1 illustrates servers 106, 108, 120 and 130 assingle computing devices, respectively, the disclosure is not solimited. For example, one or more functions of servers 106, 108, 120and/or 130 may be distributed across one or more distinct computingdevices. Moreover, in one embodiment, servers 106, 108, 120 and/or 130may be integrated into a single computing device, without departing fromthe scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating a client device showing anexample embodiment of a client device that may be used within thepresent disclosure. Client device 200 may include many more or lesscomponents than those shown in FIG. 2. However, the components shown aresufficient to disclose an illustrative embodiment for implementing thepresent disclosure. Client device 200 may represent, for example, clientdevices discussed above in relation to FIG. 1.

As shown in the figure, Client device 200 includes a processing unit(CPU) 222 in communication with a mass memory 230 via a bus 224. Clientdevice 200 also includes a power supply 226, one or more networkinterfaces 250, an audio interface 252, a display 254, a keypad 256, anilluminator 258, an input/output interface 260, a haptic interface 262,an optional global positioning systems (GPS) receiver 264 and acamera(s) or other optical, thermal or electromagnetic sensors 266.Device 200 can include one camera/sensor 266, or a plurality ofcameras/sensors 266, as understood by those of skill in the art. Thepositioning of the camera(s)/sensor(s) 266 on device 200 can change perdevice 200 model, per device 200 capabilities, and the like, or somecombination thereof.

Power supply 226 provides power to Client device 200. A rechargeable ornon-rechargeable battery may be used to provide power. The power mayalso be provided by an external power source, such as an AC adapter or apowered docking cradle that supplements and/or recharges a battery.

Client device 200 may optionally communicate with a base station (notshown), or directly with another computing device. Network interface 250includes circuitry for coupling Client device 200 to one or morenetworks, and is constructed for use with one or more communicationprotocols and technologies as discussed above. Network interface 250 issometimes known as a transceiver, transceiving device, or networkinterface card (NIC).

Audio interface 252 is arranged to produce and receive audio signalssuch as the sound of a human voice. For example, audio interface 252 maybe coupled to a speaker and microphone (not shown) to enabletelecommunication with others and/or generate an audio acknowledgementfor some action. Display 254 may be a liquid crystal display (LCD), gasplasma, light emitting diode (LED), or any other type of display usedwith a computing device. Display 254 may also include a touch sensitivescreen arranged to receive input from an object such as a stylus or adigit from a human hand.

Keypad 256 may comprise any input device arranged to receive input froma user. For example, keypad 256 may include a push button numeric dial,or a keyboard. Keypad 256 may also include command buttons that areassociated with selecting and sending images. Illuminator 258 mayprovide a status indication and/or provide light. Illuminator 258 mayremain active for specific periods of time or in response to events. Forexample, when illuminator 258 is active, it may backlight the buttons onkeypad 256 and stay on while the client device is powered. Also,illuminator 258 may backlight these buttons in various patterns whenparticular actions are performed, such as dialing another client device.Illuminator 258 may also cause light sources positioned within atransparent or translucent case of the client device to illuminate inresponse to actions.

Client device 200 also comprises input/output interface 260 forcommunicating with external devices, such as a headset, or other inputor output devices not shown in FIG. 2. Input/output interface 260 canutilize one or more communication technologies, such as USB, infrared,Bluetooth™, or the like. Haptic interface 262 is arranged to providetactile feedback to a user of the client device. For example, the hapticinterface may be employed to vibrate client device 200 in a particularway when the Client device 200 receives a communication from anotheruser.

Optional GPS transceiver 264 can determine the physical coordinates ofClient device 200 on the surface of the Earth, which typically outputs alocation as latitude and longitude values. GPS transceiver 264 can alsoemploy other geo-positioning mechanisms, including, but not limited to,triangulation, assisted GPS (AGPS), E-OTD, CI, SAI, ETA, BSS or thelike, to further determine the physical location of Client device 200 onthe surface of the Earth. It is understood that under differentconditions, GPS transceiver 264 can determine a physical location withinmillimeters for Client device 200; and in other cases, the determinedphysical location may be less precise, such as within a meter orsignificantly greater distances. In one embodiment, however, Clientdevice may through other components, provide other information that maybe employed to determine a physical location of the device, includingfor example, a MAC address, Internet Protocol (IP) address, or the like.

Mass memory 230 includes a RAM 232, a ROM 234, and other storage means.Mass memory 230 illustrates another example of computer storage mediafor storage of information such as computer readable instructions, datastructures, program modules or other data. Mass memory 230 stores abasic input/output system (“BIOS”) 240 for controlling low-leveloperation of Client device 200. The mass memory also stores an operatingsystem 241 for controlling the operation of Client device 200. It willbe appreciated that this component may include a general purposeoperating system such as a version of UNIX, or LINUX™, or a specializedclient communication operating system such as Windows Client™, or theSymbian® operating system. The operating system may include, orinterface with a Java virtual machine module that enables control ofhardware components and/or operating system operations via Javaapplication programs.

Memory 230 further includes one or more data stores, which can beutilized by Client device 200 to store, among other things, applications242 and/or other data. For example, data stores may be employed to storeinformation that describes various capabilities of Client device 200.The information may then be provided to another device based on any of avariety of events, including being sent as part of a header during acommunication, sent upon request, or the like. At least a portion of thecapability information may also be stored on a disk drive or otherstorage medium (not shown) within Client device 300.

Applications 242 may include computer executable instructions which,when executed by Client device 200, transmit, receive, and/or otherwiseprocess audio, video, images, and enable telecommunication with anotheruser of another client device. Other examples of application programs or“apps” in some embodiments include calendars, browsers, contactmanagers, task managers, transcoders, photo management, databaseprograms, word processing programs, security applications, spreadsheetprograms, games, search programs, and so forth. Applications 242 mayfurther include search client 245 that is configured to send, toreceive, and/or to otherwise process a search query and/or search resultusing any known or to be known communication protocols. Although asingle search client 245 is illustrated it should be clear that multiplesearch clients may be employed. For example, one search client may beconfigured to enter a search query messages, where another search clientmanages search results, and yet another search client is configured tomanage serving advertisements, IMs, emails, and other types of knownmessages, or the like.

Having described the components of the general architecture employedwithin the disclosed systems and methods, the components' generaloperation with respect to the disclosed systems and methods will now bedescribed below.

FIG. 3 is a block diagram illustrating the components for performing thesystems and methods discussed herein. FIG. 3 includes a quality engine300, network 315 and database 320. The quality engine 300 can be aspecial purpose machine or processor and could be hosted by anapplication server, content server, social networking server, webserver, search server, content provider, email service provider, adserver, user's computing device, and the like, or any combinationthereof. The database 320 can be any type of database or memory, and canbe associated with a content server on a network (e.g., content server106, search server 120 or application server 108 from FIG. 1) or auser's device (e.g., device 102 or device 200 from FIGS. 1-2). Database320 comprises a dataset of images, user data and associated usermetadata. Such information can be stored in the database 320independently and/or as a linked or associated dataset. It should beunderstood that the image data (and metadata) in the database 320 can beany type of image information and type, whether known or to be known,without departing from the scope of the present disclosure.

As discussed above and in more detail below, the image data/metadataprovides the basis for the raw image data (e.g., features/deepdescriptors) of the images. Such raw image data can be directly based onthe information contained in the data/metadata and associated with thecontent of an image; and in some embodiments, as discussed below, theraw image data can be derived from such image data/metadata.

For purposes of the present disclosure, as discussed above, images(which are stored and located in database 320) as a whole are discussedwithin some embodiments; however, it should not be construed to limitthe applications of the systems and methods discussed herein. Indeed,while reference is made throughout the instant disclosure to images (orpictures), other forms of user generated content and associatedinformation, including for example text, audio, video, multi-media, RSSfeed information can be used without departing from the scope of theinstant application, which can thereby be communicated and/or accessedand processed by the quality engine 300 according to the systems andmethods discussed herein.

According to some embodiments, the stored user data can include, but isnot limited to, information associated with a user's profile, userinterests, user behavioral information, user attributes, userpreferences or settings, user demographic information, user locationinformation, user biographic information, and the like, or somecombination thereof. In some embodiments, the user data can alsoinclude, for purposes rendering and/or delivering images, user deviceinformation, including, but not limited to, device identifyinginformation, device capability information, voice/data carrierinformation, Internet Protocol (IP) address, applications installed orcapable of being installed or executed on such device, and/or any, orsome combination thereof. It should be understood that the data (andmetadata) in the database 320 can be any type of information related toa user, content, a device, an application, a service provider, a contentprovider, whether known or to be known, without departing from the scopeof the present disclosure.

As discussed above, with reference to FIG. 1, the network 315 can be anytype of network such as, but not limited to, a wireless network, a localarea network (LAN), wide area network (WAN), the Internet, or acombination thereof. The network 315 facilitates connectivity of thequality engine 300, and the database of stored resources 320. Indeed, asillustrated in FIG. 3, the quality engine 300 and database 320 can bedirectly connected by any known or to be known method of connectingand/or enabling communication between such devices and resources.

The principal processor, server, or combination of devices thatcomprises hardware programmed in accordance with the special purposefunctions herein is referred to for convenience as quality engine 300,and includes a learning module 302, corpus module 304, runtime module306 and a camera module 308. It should be understood that the engine(s)and modules discussed herein are non-exhaustive, as additional or fewerengines and/or modules (or sub-modules) may be applicable to theembodiments of the systems and methods discussed. The operations,configurations and functionalities of each module, and their role withinembodiments of the present disclosure will be discussed with referenceto FIGS. 4A-7.

FIG. 4A is a process 400 diagram illustrating steps performed inaccordance with exemplary embodiments of the present disclosure forautomatically identifying high-quality content. Process 400 is performedby the quality engine 300, and specifically by the learning module 302and the corpus module 304, which are special purpose modules as detailedbelow. The quality engine 300, through models 302 and 304 can model acollection of images. Such modeling is performed for purposes ofidentifying high-quality content, which, as discussed below, can be usedfor identifying other new high-quality content for promoting suchcontent thereby remedying a “cold start” (as discussed below in relationto FIG. 5), and for selecting and/or determining high-quality capturedcontent by a camera (as discussed below in relation to FIGS. 6A-6B).

Process 400 focuses upon modeling an image database to effectuate costeffective and computationally efficient identification of high-qualityimages. Such content, as discussed herein, can be images of and/orassociated with a user generated content (UGC) collection, or imagescaptured by a camera, whether stored in the camera's memory upon captureor in-focus within the camera's lens. While the discussion hereinfocuses upon the identification of images (e.g., image data andmetadata), it should be understood that the present disclosure is not solimiting, as the disclosed systems and methods can be applied to anyknown or to be known type of content object/item, including, but notlimited to, text, video, audio, RSS feeds, or other types ofmulti-media, including any known or to be known type of message, contentobject or item, or data stored in a datastore.

Process 400 involves the training of the quality engine 300 (i.e., thelearning module 302 for Steps 402-408 and corpus module 304 for Steps410-412, discussed below) based on the raw image data of images in a UGCcollection (e.g., Flickr®). As discussed herein, Process 400 trains thequality engine 300 on data that has been collected using strong socialindicators of quality, which are then applied to content that has nosuch indicators.

According to embodiments of the present disclosure, social indicatorsare based on social data retrieved, accessed, identified, receivedand/or communicated to, from and/or between users on an electronicsocial network. Such information can be derived, determined and/oridentified from messages being transmitted and/or posted over theInternet respective to an image via any type of known or to be knownsocial media or communication platform, such as, but not limited to,Yahoo! ® Messenger, Flickr®, Tumblr®, Facebook®, Twitter®, Instagram™,Wikipedia®, or any other type of blog, microblog, news posting, orwebsite or webpage, and the like. For example, such information can becomprised within a user comment about an image posted to another user'sFlickr® page (or dashboard), which can potentially be read by the entireworld or anyone on the world-wide Flickr® community. In another example,such information can be comprised within interactions between specificusers, such as IM messages, Twitter®, Facebook® messages, and the like,which correspond to an image (e.g., tweeting an image, or a URL of animage).

In line with the above non-limiting examples, in addition to any otherknown or to be known methodology of users communicating over theInternet, such communications effectively yield knowingly and/orvoluntarily generated and/or shared content that can be analyzed,collected and utilized for a variety of purposes, as discussed herein.In some embodiments, the disclosed social data can include and/or beassociated with spatial data, temporal data and/or logical data (wherethe logical data can be extracted from the communications to determineand/or reveal a topic of such communications). Such social, spatial,temporal and/or logical data can be related to and/or generated from animage, an image's source (e.g., a network location hosting the image orthe creator of the image), and the like. For example, temporal data ofan image can be related to when the image was captured, or when theimage was posted to a user's Flickr® page. Spatial data can be relatedto the location of the image. Logical data can be related to topicaland/or a categorical representation of the image.

Process 400 begins with Step 402 where a set of images from a UGCcollection (e.g., database such as the Flickr® datastore) that satisfy asocial threshold are identified. That is, Step 402 involves analyzingthe images in the UGC collection to identify at least a predeterminednumber of images in the UGC collection that have associated social datasatisfying the social threshold. Such analysis can occur across theentire UGC collection, per user, or for a determined subset of images(e.g., images tagged or labeled as being related to a category). Thesocial threshold serves as a minimum threshold for identifying imagesthat have been subject to a particular amount of social activity (e.g.,having the requisite social indicators for training the quality engine300).

In some embodiments, the identification of images having the requisitesocial data associated therewith can be based on the image beinginvolved in a certain amount of social activity over a period of time ator above the social threshold. For example, if an image has been shared,retweeted, favorited, saved, and the liked, a certain number of timesover a time period at or above the social threshold, then such imagewould be identified in Step 402.

In some embodiments, Step 402 may involve identifying images from knownimage creators (e.g., famous or recognizable artists, publishers,content hosts, and the like). For example, the movie studio MGM®typically posts pictures associated with upcoming movies on theirTumblr® page. As a result, MGM's account typically has a reliable numberof followers and therefore a high volume of social activity respectiveto such images; thus, for example, Step 402 can involve identifyingimages from MGM's Tumblr® page as the set of images.

Step 404 then involves determining image heuristic data for each of theimages in the identified set from Step 402. In some embodiments, thedetermination of heuristic data is based on a calculation of the socialdata of each image respective to a number of views the image (or pagehosting the image) receives:

Social Data/# of Views

By way of a non-limiting example, if an image has associated social dataindicating that the image receives 20 shares after receiving 30 views,then the image heuristic data for that image would involve informationindicating that two-thirds of the time the image is shared upon viewing.

In some embodiments, the determination of heuristic data is based on acalculation of image data of each image relating to the features ordescriptors of the image. For example, analysis of an images data,whether it is the resolution, pixel quality, size, dimension, colorscheme, exposure, white balance (or other type of image characteristic)would reveal if the image's picture quality satisfies the qualitythreshold (e.g., the aesthetic quality of the image is at a certainlevel).

For purposes of this disclosure, the discussion will focus on acalculation of image heuristic data being based on the social data of animage (e.g., a number of views); however, one of skill in the art shouldunderstand the instant disclosure is not so limiting. That is, anynumber of known or to be known data calculations, combinations and/orpermutations associated with an image can be utilized to determine theimage heuristic data for an image. In some embodiments, any known or tobe known real-time (or near real-time) data analytics, measurementsand/or weighting factors used for understanding a context of an image'sinfluence on a network (or across other users) can be utilized herein.Indeed, Step 404 (and Step 406 below) can involve sophisticated dataanalysis (for distinguishing image data between images in a UGCcollection), time consideration (for determining when certain images aresignificant based on temporal data of the image and the currenttime/date, which can be based on a context, content and/or sentiment ofthe image), influence analysis (for understanding the potential impactcertain users have on the image being viewed by others), and networkanalysis (for determining how the image migrates or propagates across anetwork).

In Step 406 a determination is made regarding the quality of the imagesin the identified set of UGC images based on the analysis in Step 404.As discussed above, an image can be determined to be “high-quality” wheninformation associated with the image indicates that the image is ofinterest to a number of users, contains a certain picture quality, isassociated with a particular artist or some combination thereof, at orabove the quality threshold. In some embodiments, the determination ofan image's quality results in the image being tagged or embedded withmetadata indicating the determination's result, and/or havinginformation indicating the image's quality stored in association withthe image. In some embodiments, such tagging/metadata can relay a zeroor “0” indicating that the image does not satisfy the quality threshold,and in some embodiments, a one or “1” can be associated with the imageindicating that the image is a high-quality image (thereby satisfyingthe quality threshold).

In some embodiments, if an image's heuristic data falls below thequality threshold, then the image would be designated as not“high-quality”. For example, building on the example above, the imagereceived 20 shares from 30 views, resulting in a 66% share rate. If thequality threshold was set at 70%, then this image would not bedetermined to be of “high-quality”—it does not have the requisiteinterest from users on a site, network, group or category, for example.Therefore, the image would be tagged, embedded or otherwise associatedwith information designating the image as a “0”. In another non-limitingexample, if the quality threshold was set at 60%, then the image wouldbe designed with a “1” indicating the image as being “high-quality.”

In Step 408, the identified high-quality images are then analyzed toidentify raw image data within/among such images. The analysis occurringin Step 408 involves parsing each high-quality image and extracting theraw image data. In some embodiments, such analysis involves applying a“deep learning” algorithm to the high-quality images to determine theraw image data of each image (i.e., the features and descriptors).

In accordance with embodiments of the present disclosure, “deeplearning” (also referred to as deep structured learning or hierarchicallearning) involves machine learning algorithms that model high-levelabstractions in data by using model architectures composed of multiplenon-linear transformations. Deep learning is part of a broader family ofmachine learning methods based on learning representations of data. Animage can be represented in many ways such as a vector of intensityvalues per pixel, or in a more conceptual way as a set of edges, regionsof particular shape, and the like. The implementation of deep learningas part of the disclosed systems and methods enables the replacement ofhandcrafted features with efficient algorithms for unsupervised orsemi-supervised feature learning and hierarchical feature extractionfrom images.

In some embodiments, Process 400 (i.e., Step 408) can be implementedusing any known or to be known deep learning architecture or algorithmictechnique, such as, but not limited to, deep neural networks, artificialneural networks (ANNs), convolutional neural networks (CNNs), and deepbelief networks can be utilized herein. According to some embodiments,as discussed in more detail below, the disclosed deep learningmethodology employs CNNs (however, it should not be construed to limitthe present disclosure to only the usage of CNNs, as any known or to beknown deep learning architecture or algorithm is applicable to thedisclosed systems and methods discussed herein). CNNs consist ofmultiple layers which can include: the convolutional layer, rectifiedlinear unit (ReLU) layer, pooling layer, dropout layer and loss layer,as understood by those of skill in the art. When used for imagediscovery, recognition and similarity, CNNs produce multiple tiers ofdeep feature collections by analyzing small portions an image.

For purposes of this disclosure, the discussion will reference vectorrepresentations of images (as discussed below); however, it should notbe viewed as limiting as any type of known or to be known machinelearning or deep learning analysis and/or transformation is applicableto the discussion herein without departing from the scope of the presentdisclosure.

For purposes of this disclosure, raw image data, referred to as featuresand/or descriptors can include, but are not limited to, visualcharacteristics of the images characterized (or categorized and labeled)by color features, texture features, type features, edge features and/orshape features, and the like. The results of these collections are thentiled so that they overlap to obtain a better representation of theimage; which is repeated for every CNN layer. CNNs may include local orglobal pooling layers, which combine the outputs of feature clusters.

One advantage of CNNs is the use of shared weight in convolutionallayers; that is, the same filter (weights) is used for each pixel ineach layer, thereby reducing required memory size and improvingperformance. Compared to other image classification algorithms, CNNs userelatively little pre-processing which avoids the dependence onprior-knowledge and the existence of difficult to design handcraftedfeatures.

It should be understood by those of skill in the art that the features(or descriptors or deep descriptors) of images can include any type ofinformation contained in, or associated therewith, image data, videodata, audio data, multimedia data, metadata, or any other known or to beknown content that can be associated with, derived from or comprisedwithin the content item (or media file). In some embodiments, suchfeature data can be audio data associated with an image (or media file)that plays when the image is viewed, for example. In another example,feature data can include comments or user feedback (e.g., comments on asocial network) that is associated with not only the image file, butalso data associated with the source of the file.

In Step 410, the quality engine 300 is trained to identify informationassociated with the identified raw image data extracted in Step 408.Step 410 involves training the learning module 302 with information thatmodule 302 can use when analyzing another subset of images.

According to some embodiments, the training occurring in Step 410involves the corpus module 304 analyzing the remaining images in the UGCcollection (i.e., those images not identified in Step 402) to determinewhich images have corresponding raw image data to the identifiedhigh-quality raw data from Step 408. Such analysis of the remaining UGCcollection is performed in a similar manner as discussed above inrelation to Step 408. The remaining images are parsed and their rawimage data is extracted. Then, a comparison is performed between the rawimage data identified in Step 408 and the raw image data of theremaining images. If an image has raw image data matching that of anidentified high-quality image (at or above a comparison threshold), thenthat image is classified as “high-quality” in a similar manner asdiscussed above in relation to Step 406. Such comparison can beperformed by any known or to be known machine learning algorithm,computational analysis, statistical analysis or technology, such as, butnot limited to, vector analysis, cluster analysis, data mining, Bayesiannetwork analysis, Hidden Markov models, artificial neural networkanalysis, logical model and/or tree analysis, and the like.

According to some embodiments, Step 410 involves the determination ofraw image data for each image in the UGC collection (or within anothersubset of the UGC collection). As illustrated in FIGS. 4B-4C, theprocess of Step 410, in some embodiments, includes steps (or sub-steps)450-460 and 470-480, respectively.

According to some embodiments, as in FIG. 4B which details anon-limiting embodiment of Step 410, Step 450 involves parsing each UGCimage to identify the raw image data for each UGC image, as discussedabove. In Step 452, the raw data for each image (the high-quality imagesand the other UGC collection images) is then translated into an imagefeature vector having a dimensional value proportional to the pixelvalue of each image. According to some embodiments of the presentdisclosure, the feature vector is a result of forward propagation of theraw data values through the layers of the CNN. For example, using a 4096pixel product image as the test image, such propagation results in a4096 dimensional feature vector. In Step 454, the feature vector istransformed into an m-bits hash-code (e.g., a deep hash code (DHC) orbyte array) by applying any known or to be known Eigen-hash algorithm(e.g., spectral hashing) to the feature vector. As understood by thoseof skill in the art, an Eigen-hash algorithm can produce compact m-bitcodes from data-points of a feature vector so that the Hamming distance(or other known or to be known type of distance calculation) betweenm-bit codes correlates with a semantic similarity. That is, in Step 456,a determination is made regarding which images correspond to similarcontent (or a similar category). This determination can be based onanalysis of the m-bit codes for each image, as discussed above. Then, inStep 458, for each image in the same category, the image feature vectorsof those images identified as “high quality” (as discussed above) withthe remaining UGC images in the same category are compared. As in Step460, this comparison results in a semantic similarity being determinedbased on the comparison of feature vectors, as discussed above. That is,if the semantic similarity of data points of each vector is at or abovethe comparison threshold for a comparison between an establishedhigh-quality image (from Step 406) and a UGC collection image, then theUGC collection image is classified as high-quality. As discussed above,this comparison can be based on any type of known or to be known deeplearning, comparison or data mining algorithm.

According to some embodiments, FIG. 4C details another non-limitingembodiment of Step 410. The process begins with Step 470 which involvesparsing each UGC image to identify the raw image data for each UGCimage, as discussed above. In Step 472, the raw data for each image isutilized to determine (or identify) the different “quality buckets” (orcategories or distinct collections of related data) the UGC images maycorrespond to. In some embodiments, the identity of each UGC bucket canbe based on any known or to be known type of compilation heuristicapplied to the raw image data. For example, the raw data for each imagemay indicate social signals associated with the image, which can bedetermined to be reliable upon an indication that such social signalssatisfy an interaction threshold—for example, a lower bound on the totalnumber of views. In some alternative embodiments, the identity of animage may be based on annotations associated with the media—annotationsby users, a capturing, creating, communicating and/or renderingcomputer, or some combination thereof. In some embodiments, theresultant quality buckets of Step 472 can be referred to as “labeleddata” that can be used for training.

In Step 474 a model is then trained to predict which of the determinedquality buckets a test image falls into. Such training can be performedby any known or to be known machine learning classification methodology,algorithm or technology, for example, but not limited to, stochasticgradient descent on a log-likelihood cost function. As such, it shouldbe understood by one of ordinary skill in the art that FIG. 4C is theembodiment of a machine learning classifier integrated with or otherwiseworking along with or in communication with or as an ancillary functionof a deep neural network of UGC image data.

In some embodiments, the training in Step 474 involves the creation of afeature vector for the test image (and images to which the test imagewill be compared). As discussed above, the raw image data is thetranslated into points forming an n-dimensional vector. In someembodiments, the feature vector for an image is the transformation ofthe image's pixel data into vector points, as discussed above; however,the feature vectors discussed herein can have “separating hyperplanes”on top of the space of all created feature vectors. The “separatinghyperplanes” provide boundaries of data, where such boundaries can beindicative of the “bucket” (by way of non-limiting example a label) ofthe image, or annotations of the image, or other factors derived,determined or otherwise identified from the raw image data (by way ofnon-limiting example social data associated with the image), asdiscussed herein.

In Step 476, a probability analysis is performed to determine whetherthe proper classification of the image(s) will occur based on thecreated feature vector(s). According to some embodiments, in a similarmanner as discussed above, analysis of the feature vectors, per bucket,against a test image, involves a comparison of the data points along therespective feature vectors. In some embodiments, such comparison canalso include a comparison of the hyperplanes' of each image. Suchanalysis can be performed by any known or to be known computerizedprobability algorithm or technology, such as, but not limited to,probability bounds analysis, Bayes analysis, risk analysis, Monte Carloanalysis, and/or any other type of known or to be known algorithmicprobability analysis.

In Step 478, a score for the test image is determined based on theprobability analysis occurring in Step 476. The score is comparedagainst a quality threshold in order to determine the quality of theimage(s). Step 480. In some embodiments, the test image is determined tobe “high-quality” (in accordance with an identified bucket) if the scoresatisfies the quality threshold; however, if the score falls below thethreshold, then the image can be identified as “low quality”. In someembodiments, the scores of entire labeled images (or images in a UGCbucket) can be combined into a single quality score for the purpose ofranking each image within the set (or bucket), such that a designationof each image's quality can be determined to be “high quality” or “lowquality”.

In Step 412, a category determination is made for each high-qualityimage. In some embodiments, such determination can be made respective toeach high-quality image identified in the UGC collection; and in someembodiments, such determination can be made respective to thehigh-quality images from the set of images; in further embodiments, sucha determination can be made directly from a trained model, as discussedabove. Therefore, if an image's raw data indicates that the image'scontent is associated with a particular topic, then a label can beapplied to the image (either as a tag, embedded data/metadata, or someother known or to be known form of category identification). Accordingto some embodiments, the determination of a category can be based on asimilar analysis of the raw data for determining if an image is“high-quality,” as discussed above in relation to Steps 406-408. Thatis, a feature vector can be produced based on the image, and analysis ofthe features respective content of the image can indicate a type ofcontent of the image. For example, if an image's vector content code(s)falls within an m-bit dimension associated with “finance”, then thatimage would be categorized as “finance.” In some embodiments, accordingto the classifier framework embodiments discussed above, the probabilitythat the model assigns the label “finance” directly forms the basis uponwhich the image would be categorized as “finance.”

Turning to FIG. 5, Process 500 details the steps for implementing thetrained quality engine 300 (from Process 400) for identifying a new,high-quality images. Process 500, which is performed by the runtimemodule 306 of the quality engine 300, details the computerizedmethodology for identifying an image in a UGC collection that does nothave the applicable social indicators and/or identifiers indicating thecontent's quality and/or user engagement/interest (e.g., a newly createdand/or uploaded image). According to some embodiments, as discussedbelow, the focus of Process 500 is to identify new, high-quality imagesthat can be promoted to others users as recommended content, and/orutilized for marketing purposes.

By way of non-limiting example of Process 500 (and Process 400), solelyin order to provide an illustrative embodiment of the presentdisclosure, user Bob is at the NBA® Finals and takes a picture of LeBronJames on the court. Bob uploads this picture to his Flickr® page. Sincethis picture was recently uploaded, and is associated with a topicalsubject given that the NBA finals are currently on going, the imagewould likely be a source of interest not only to Bob's friends onFlickr® (e.g., the users that follow his images), but also across thenetwork. As such, Process 500 can apply knowledge from the qualityengine 300 (as discussed above) and identify that Bob's new picture is“high-quality” (e.g., it corresponds to a topic of interest to manyusers). As discussed in more detail below, Bob's picture can berecommended to other users. In some embodiments, Bob's picture can beutilized as a basis for serving promotions (e.g., ads) as it isdiscovered that Bob's picture is of interest to many users (i.e.,“high-quality”) and as a result an advertiser could leverage suchinterest in implementing a marketing strategy around the picture'spresence on Flickr®.

For purposes of this discussion, the identification of content willfocus on analyzing and discovering digital images; however, it shouldnot be construed as limiting the scope of the instant application tosolely images, as any known or to be known type of content or media(e.g., text, video, audio, multi-media, RSS feeds, and the like) areapplicable to the disclosed systems and methods discussed herein.

Process 500 begins with Step 502 which is based on an identified newimage to a UGC collection (e.g., a user's page on Flickr®). As discussedabove, a “new” image refers to an image that has been recently createdand uploaded to a UGC collection, whereby the presence of such image ona user's account page (e.g., Flickr® page) is a recent occurrence (i.e.,the time the content was updated to the current time satisfying arecency threshold). In some embodiments, “new” may also refer to animage being downloaded, shared, re-blogged/re-posted, favorited and thelike; however, for purposes of this discussion, Process 500 will dealwith an newly uploaded image; however, it should not be construed aslimiting, as any known or to be known manner of creating and/or postingdigital content can be utilized herein without departing from scope ofthe instant disclosure.

In some embodiments, Step 502 involves analyzing the images in the UGCimage collection to identify images that do not have the requisitesocial indicators satisfying the social threshold, as discussed above inrelation to Steps 402-404. That is, for example, images that are notcurrently being followed, shared or otherwise communicated between usersor posted as messages at or above a threshold value. Such images, asdiscussed above, can be identified as “new” images. In some embodiments,Step 502 can be triggered upon a search for new content, whether from auser or an application; however, for purposes of this disclosure thediscussion will focus on a user uploading an image; however, it shouldnot be construed as limiting as any event can trigger Process 500beginning to identify high-quality images as discussed herein.

Upon upload, Step 502 further involves analyzing the new image toidentify the new image's raw image data. As discussed above, suchanalysis involves parsing the image and extracting the raw image data(e.g. features and descriptors of the image). That is, in someembodiments as discussed above, Step 408 involves applying a deeplearning algorithm (or any other known or to be known data extractionalgorithm) to the new image to determine the raw image data of the newimage.

In Step 504, a determination is made as to a category the new image isassociated with. In some embodiments, this can be determined, derived,or otherwise identified from a tag the uploading user associates withthe image (for example, a hashtag). In some embodiments, the categorydetermination is based on analysis of the raw image data identified inStep 502. That is, in a similar manner as discussed above in relation toStep 412, analysis of the new image's raw data can indicate that theimage's content is associated with a particular topic, whereby a labelcan be applied to the image (either as a tag, embedded data/metadata, orsome other known or to be known form of category identification).According to some embodiments, the determination of a category can bebased on a similar analysis of the raw data for determining if an imageis “high-quality,” as discussed above in relation to Steps 406-412. Forexample, the image's raw data can be translated into a feature vector,and analysis of the features of the image represented by the vector canindicate a type of content of the image, as discussed above. Forexample, an image can be categorized as personal, news, sports, family,business, finance, other, medical, banking, important, and the like, orany other type of category classification.

In Step 506, a determination is made regarding whether the new image is“high-quality.” Such “high-quality” analysis is performed in a similarmanner as discussed above in relation to Step 410. That is, the rawimage data of the image is compared against raw image data ofhigh-quality images in the UGC collection in the same category. In someembodiments, the comparison can be across the entire UGC collection,which thereby alleviates Process 500 from performing Step 504.

According to some embodiments, as discussed above, Step 506 involvescomparing the raw image data of the new image and the raw image data ofthe high-quality images in the UGC collection that are classified in thesame category. If the new image has raw image data matching that of anidentified high-quality image (at or above a comparison threshold), thenthe new image is classified as “high-quality” in a similar manner asdiscussed above in relation to Steps 406 and 410. According to someembodiments, the comparison can involve feature vectors associated withthe images being compared, as the raw image data for each image can betranslated to feature vectors by applying any known or to be known hashand/or probability algorithm, as discussed above. According to someembodiments, such comparison can instead be performed by any known or tobe known machine learning algorithm, computational analysis, statisticalanalysis or technology, such as, but not limited to support vectormachine training, vector analysis, cluster analysis, data mining,Bayesian network analysis, Hidden Markov models, artificial neuralnetwork analysis, logical model and/or tree analysis, and the like.

In Step 508, a similarity (or similarity score) is determined for thenew image. That is, in some embodiments, the new image is scored aseither a “0” or “1” as a result of the comparison in Step 506, asdiscussed above in relation to determining whether the image is“high-quality.” As discussed above, the “0” denotes that the new imageis not “high-quality” and the “1” denotes that the image is“high-quality.” Such determination can be based on a number of featuresof the features vectors matching in accordance with a comparisonthreshold, as discussed above. That is, according to some embodiments,if the feature vector for the new image matches the feature vector for ahigh-quality image in the same category at or above the comparisonthreshold, then the new image is identified as “high-quality” andProcess 500 proceeds to Step 514, as discussed below. If the featurevector for the new image matches the feature vector for a high-qualityimage in the same category below the match threshold, then the new imageis not identified as “high-quality” and Process 500 proceeds to Step510, as discussed below. In either instance, the new image is tagged,embedded, or otherwise identified with information (data or metadata)indicating the outcome of the quality determination of Step 508 (andsuch information is stored in a datastore associated with the UGCcollection).

Step 510 involves monitoring the new image's heuristic data (when theimage is identified as not “high-quality”). Such monitoring involvesperiodically analyzing the social data associated with the image todetermine whether the image has developed an interest within a communityor group of users. The monitoring occurring in Step 508 can be performedaccording to a predetermined time period or according to detection ofactivity by a user(s) with respect to the location of the new image. Theanalysis of the social data of the new image is performed in a similarmanner as discussed above in relation to Step 402-406. Such monitoringenables the quality engine 300 to determine if an image's quality (e.g.,interest among other users) changes over time. For example, if a userposts an image from his trip to the Lincoln Memorial in Washington, D.C.in April, this image may not be of interest to a variety of users(besides the user's family). However, in the following February thisimage may become interesting to other users (at or above the qualitythreshold) because of President's Day.

In some embodiments, when the heuristics/social data of the new imageare determined to satisfy the quality threshold, the new image can bepromoted. Step 512. Promotion or recommendation will be discussed inmore detail below in relation to Step 514.

Step 514 involves promoting the new image due to its determination ofbeing “high-quality.” According to some embodiments, promotion (as inSteps 512 and 514) can involve sending or recommending the new image toa user or set of users determined to be interested in the new image, oralerting such user(s) to the new image. In some embodiments, promotioncan also involve promoting the new image for marketing and/or licensingopportunities. In either situation, the users, marketers and/orlicensors (referred to as users below for simplicity) would be requiredto be determined to be interested in the new image (e.g., interested inthe new image's content).

As such, according to such embodiments, Steps 512/514 can involvedetermining a set of users (or a single user) to alert regarding the newimage. According to embodiments of the present disclosure, Steps 512/514involve determining which users on a network would find the new image tobe of interest to them. Such determination is based on an analysis ofthe user data stored in database 320. As discussed above, user data canbe associated with, identified and/or derived from any type ofcommunication platform, communication platform, content provider and thelike (such as, a user's Flickr® account). The user data can include, butis not limited to, information associated with a user's profile, userinterests, user behavioral information, user attributes, userpreferences, user demographic information, user location information,user biographic information, and the like, or some combination thereof.Such user data can reveal the user's online activity—i.e., what the useris reading, where the user is located, what the user is writing (orsharing online), how the user consumes information on a device, networkor platform, and the like.

Thus, Steps 512/514 involve the application of any type of known or tobe known algorithm, technique or technology related to statisticalanalysis, data mining, hash tree analysis, vector analysis, behavioralanalysis and/or targeting analysis and the like. As such, Steps 512/514involve analyzing the user data specific to a user to determine whetherthe user has expressed an interest in the new image.

According to some embodiments, such analysis includes, for example,determining if specific types of user data relating to the new image'scategory designation are present within the set of user data of theuser. Such analysis can include, for example, formulating ann-dimensional vector to represent a user's interest (from the user data)and an n-dimensional vector to represent the new image (from the rawimage data—or using the vector created above in Steps 504-508).Comparison of the vectors to one another resulting in an overlap ofvector points (or coordinates) at or the above the quality thresholdwould reveal the user's interest in the new image (where, as discussedabove, the quality threshold represents a minimum level of dataidentifiable from the analyzed user data that corresponds to the imagedata of the new image). Satisfaction of the quality threshold indicatesthat a user has, has expressed, and/or has indicated implicitly orexplicitly through user input, user behavior, user settings, and thelike, and/or some combination thereof, a likelihood that the user wouldbe interested in the new image.

According to some embodiments, the promotion occurring in Steps 512/514can involve alerting the interested users to the identity of the artistthat created the new image. As such, the promotion occurring in Steps512/514 results in identifying new, high-quality images in the UGCcollection, in addition to identifying the artists (or creators) of suchnew, high-quality images. In some embodiments, the identification ofnew, high-quality images can be utilized when performing a search forcontent within the UGC collection (or within or across domains), as thenew, high-quality content can be ranked higher or lower in the searchresult. Such ranking can be based on a system and/or user's preferencesin discovering new content as opposed to consistently being shown thesame content from a variety of search queries for the same content (orcategory of content).

FIGS. 6A-6B are flowcharts illustrating steps performed in accordancewith some embodiments of the present disclosure for applying the trainedknowledge of the quality engine 300 for identifying high-quality imagesbeing captured or in-focus by a camera in real-time (or near real-time).FIG. 6A discusses Process 600 for identifying high-quality images thathave already been captured by the camera-enabled device. FIG. 6Bdiscusses Process 650 for identifying high-quality images that arein-focus in the lens of a camera prior to actually taking the picture(or capturing the picture), whereby a user can be alerted to when ahigh-quality image is in-focus or the camera can be instructed toautomatically capture such image based on such high qualitydetermination. Processes 600 and 650 are performed by camera module 308.

For purposes of this discussion, the identification of content willfocus on capturing digital images; however, it should not be construedas limiting the scope of the instant application to solely images, asany known or to be known type of content or media (e.g., text, video,audio, multi-media, and the like) are applicable to the disclosedsystems and methods discussed herein.

Turning to FIG. 6A, Process 600 begins with Step 602 where a set ofimages are captured. In some embodiments, the image capture occurring inStep 602 can involve a user taking a picture using a camera enableddigital device (e.g., mobile phone, wearable device or digital camera orthe like). The number of images in the captured set of images can be inaccordance with a predetermined number of captured images, which can beset by a user, in accordance with capabilities of the application beingutilized to trigger the camera to capture the image(s), according tocapabilities of the camera (or device) itself, or some combinationthereof. In some embodiments, the captured set can be a number ofrecorded image frames (e.g., a video capture). In some embodiments, theset can be captured during a “burst” of image capture events when thecamera is in “still camera mode” (e.g., continuous, high-speed digitalimage capture for capturing photographs).

The discussion of Process 600 will deal with the identification of asingle image from a set of captured images. It should be understood,however, for purposes of this discussion, Process 600 can be performedfor identifying multiple (or a subset) high-quality images satisfyingthe quality threshold from the captured set. Additionally, in someembodiments, Process 600 can be performed for a single captured image.That is, Process 600 can involve determining if the quality of a singlecaptured image (e.g., Step 602) satisfies the quality threshold, wherebythe captured image would be discarded if it is determined to not satisfythe quality threshold (i.e., not being “high-quality”). However, forpurposes of this discussion, Process 600 will be discussed in relationto a set (or series) of captured images, which should be understood tonot limit the scope of Process 600.

In Step 604, the captured set of images is analyzed to identify the rawimage data associated with each image. The determination of the rawimage data for each captured image can be performed in accordance withthe Steps 408-410 of Process 400, and Step 502 of Process 500, interalia, outlined above in relation to FIGS. 4A-5. That is, as discussedabove, the captured images are analyzed in order to extract, determineor otherwise identify the raw image data (i.e., features/deepdescriptors) of each captured image. According to some embodiments,these deep descriptors can be formulated into a feature vector for eachimage, whereby the hash function discussed above is applied so as todetermine the m-bit codes for each image. According to otherembodiments, these deep descriptors would feed into a classificationlayer, and a direct prediction can be made for the raw image data.

According to some embodiments, the determination of the captured images'm-bit codes can be performed on the capturing device or on a server orother device in communication with the capturing device. That is, insome embodiments, the image can be processed through a locally orremotely stored application that produces the encoding result. As such,in some embodiments, the quality engine 300 can be embodied as a mobileor desktop application or “app”, downloadable from a mobile or remoteonline store. In some embodiments, the quality engine 300 can be aweb-based application requiring no download of data, in that a usersimply must access the site at which the application is located. Inembodiments, quality engine 300 can be located on/at a server (or at aremote location on a network). Thus, in such embodiments, Step 604involves the captured images (or captured image data and metadata) beingcommunicated to the server/location for determination of the featurevectors/m-bit codes of each image, and after such computation, suchinformation can be communicated back to the capturing device.

In Step 606, the formulated vectors of the captured images are comparedagainst vectors of determined high-quality images. That is, as discussedabove, Process 400's determined “high-quality” images in the UGCcollection. These identified “high-quality” images were analyzed andfeature vectors were formulated according to each image's raw data.Thus, as in Step 606, the formulated feature vectors of the capturedimages are compared against these “high-quality” feature vectors.

According to some embodiments, Step 606 can involve first identifyingwhich content category the captured images are associated with. Suchcontent category determination can be performed in similar manner as thecategory determination discussion related to Step 412 of Process 400,and Steps 504-508 of Process 500. That is, for example, the m-bit codesof each captured image are utilized as a search query to identify otherm-bit codes of “high-quality” images in the UGC collection. A similarityat or above a similarity threshold between m-bit codes indicates that animage is associated with a particular category. Thus, as discussedherein, Step 606 can involve first identifying which content categorythe captured images correspond to, and then comparing the featurevectors of the captured images to the “high-quality” feature vectorsassociated with those images in the same category.

Step 606's comparison of feature vectors of images involves, accordingto some embodiments, performing a distance calculation between thepoints of each feature vector. That is, Step 606 involves performing asimilarity analysis between the raw image data points of each capturedimages' feature vector with those data points of the “high-quality”images. Such similarity or distance (or calculation) can be computedaccording to any known or to be known distance calculation algorithm,including, but not limited to, Euclidean distance algorithm, Chebyshevdistance algorithm, Hamming distance algorithm, Mahalanobis distancealgorithm, and the like. The distance calculation is performed inaccordance with a similarity threshold, which can be set by the system,administrator, user, content server, quality engine 300, or somecombination thereof. Performance of the similarity determination in Step606 can eliminate certain results that do not satisfy (at least equalto) a similarity threshold—i.e., those images that do not have at leasta threshold amount of raw data similar to those previously determined tobe “high-quality” images (of the same type/category). For example, ifthe comparison between a captured image's deep descriptors and adatabase image's descriptors falls below the threshold, then such imagewould be identified as not being “high-quality”, and in someembodiments, may be identified accordingly, deleted, or evenautomatically deleted upon such determination.

In some embodiments, the analyzed (i.e., compared) captured images canbe ranked, so that the user can be alerted to the quality of eachcaptured image. Step 608. Such ranking is beneficial to the user so thatthe user can identify which images are “high-quality” and which imagesare not. Such identification may be necessary as the user may want toselect a captured high-quality image, but not the most high-qualityimage based on bandwidth, storage or other resource conservationconsiderations of the user's device, user account, or networkavailability/capability, and the like. According to some embodiments,the ranking is based on the above similarity determination, whereby theresults with the higher similarity value to the “high-quality” imageswill be ranked higher than those with a lower value. Indeed, some imageshaving a similarity value below a threshold may be removed from theimage capture results or provided on a subsequent page/screen.

In Step 610, the results of the comparison in Step 606, and in someembodiments, the ranking in Step 608 are provided to the user. In someembodiments, the captured images determined to be high-quality (e.g.,the ranked set of high-quality images) can be presented to a user on thecapturing device, in an associated application, or in a web-baseddocument page, and the like. In some embodiments, the captured imagesdetermined to be “high-quality,” or the most “high-quality” capturedimage can be automatically uploaded to the UGC collection (e.g., theuser's Flickr® page). In such embodiments, the uploaded image(s) maythen be subject to Process 500's analysis. In some embodiments, the usercan be presented with the ranked set of images, which may or may notinclude those images determined not to currently be “high-quality”,whereby the user can then identify which images to save, store, share,upload and/or download, and the like.

Turning to FIG. 6B, Process 650 begins with Step 652 where the imagedata currently being viewed by a camera lens (i.e., optically sensed,viewed or present within the camera lens) is identified. Step 652involves analyzing such image data in order to determine the raw imagedata associated with the image “in focus” in the camera lens (or beingread by the camera sensor). The analysis, parsing, extraction, orotherwise identification of such raw image data is performed in asimilar manner as discussed above in relation to Steps 408-410 ofProcess 400, Step 502 of Process 500, and Step 604 of Process 600, interalia, as outlined above in relation to FIGS. 4A-6A.

According to some embodiments, as understood by those of skill in theart, the optical control system of a camera controls the lens forauto-focusing the camera lens or enabling manual focusing by a user.Focusing of the camera lens, specifically auto-focusing, can beperformed by any type of known or to be known active, passive or hybridvariant, or trap focus (and the like) functionality implemented by thecamera device.

As understood by those of skill in the art, an image that is “in focus”in the camera lens, as in Step 652, refers to image data generated froma collection of pixels collectively associated with light raysoriginating from a geographic point. The light rays are passed throughthe camera's lens and converge on a sensor(s) (e.g., sensor 266 of thedevice 200), whereby the sensor, through a digital image processor(and/or processing application) can produce image data that can bestored in the camera device's flash or temporary memory. Such image datais produced without actually taking the picture, as such image data canbe viewed through a camera's lens, or through a viewfinder or viewscreen associated with a camera.

According to some embodiments, the image data does not need to be“focused” as understood by those of skill in the art (e.g., notblurred); however, for purposes of this discussion, with the advent ofalmost all commercially available camera devices utilizing some type ofauto-focus technology, the instant disclosure highlights “focused”images within a camera lens or sensor; however, it should not beconstrued as limiting, as a “blurred” images or a camera deviceimplementing known or to be known filtering, auto-focus and/or imagecapturing technology are applicable to the disclosed systems and methodsdiscussed herein.

As a result of the identification of the raw image data of the“in-focus” image, as in Step 652, Steps 654-660 of Process 650 areperformed in a similar manner as Steps 602-610 of Process 600 asdiscussed above. That is, the raw image data from Step 652 is utilizedto formulate a feature vector and m-bit code for the image. Step 654.Then, the feature vector is compared to other high-quality images todetermine the image's quality. Step 656. In some embodiments, thecomparison may be locally performed based on other determined“high-quality” images stored on the capturing device. In someembodiments, the comparison is performed respective to the images in aUGC collection, as discussed above. According to some embodiments, thecomparison of the image data occurring in Step 656 may also include andbe based on a category determination of the image data, as discussedabove.

In Step 658, a determination is made regarding the quality of the imagedata that is “in focus”. That is, a determination is made as to whetherthe “in focus” image is “high-quality” or not. Such determination isbased on the comparison of feature vectors, and performed in a similarmanner as discussed above in relation Steps 506-508 of Process 500. Ifthe image data corresponds to a “high-quality” image, as a result of theStep 658 determination, then, in some embodiments, the device canautomatically capture the image. Step 660. That is, if the “in focus”image is determined to be “high-quality,” then the device can beinstructed to automatically capture the image. In some embodiments, theautomatic capture of the image may result in the image beingautomatically uploaded to the UGC collection, whereby the analysis ofProcess 500 can then be performed on the image.

In some embodiments, the determination of “high quality” imagery canalso be utilized to optimize parameters of an image filter, as commonlyused on Flickr®, Instagram™ and the like, where optimized filters can beimplemented per site, or per user. Such “high quality” imagerydeterminations can be based on the determinations occurring in Processes400-650, as discussed above. As discussed above, such optimization canbe effectuated on the client-side application, or on the server-sideupon uploading the picture to the website associated with theapplication being used to capture the photograph. For example, thecamera can be configured to automatically apply filters so that only“high-quality” images are captured. That is, the camera canautomatically apply a filter to the “in focus” image (prior to capture)so that the image is modified to a “high quality” level, where suchlevel can be set by the camera device, quality engine 300, user or somecombination thereof. In some embodiments, the filtering of an image canautomatically occur immediately after, or upon capturing the image.

In some embodiments, a “high-quality” image determination, as in Step658 may result in an alert being provided to the user. Such alert can bea sound, vibration, visual alert, or some combination thereof, triggeredon the capturing device to alert the user that a “high-quality” image is“in focus”. The user can then determine whether to actually capture theimage or refocus or redirect the camera.

In some embodiments, if the “in focus” image is determined to not be“high-quality”, then the image may still be captured, and displayed tothe user so that the user can make the final decision. In suchinstances, information can be provided to the user upon such displayindicating the quality determination of such image. In some embodiments,a determined “in focus” non-high-quality image may be automaticallydiscarded, where capturing device can be instructed by the qualityengine 300 to delete the image data captured in Step 652. In someembodiments, such deletion may be resultant a user instruction afterprompting the user with such deletion decision, as above, or may beautomatic based on the quality determination.

According to some embodiments of the present disclosure, the discovery,determination and identification of new and/or high-quality imagesdiscussed above in relation to Processes 500, 600 and 650 can involveiterative or recursive bootstrapping or aggregation functionality,whereby the results of such Processes can be fed back to the qualityengine 300 training (as in Process 400) to improve the accuracy of theanalyzed and implemented information. Indeed, embodiments of the presentdisclosure involve the quality engine 300 applying suchrecursive/bootstrapping functions utilizing any known or to be knownopen source and/or commercial software machine learning algorithm,technique or technology.

FIG. 7 is a work flow example 700 for serving relevant advertisementsbased on the image (or content) being captured and/or promoted to auser, as per the result of FIGS. 5-6B discussed above. For example, insome embodiments, FIG. 7 illustrates how advertisements are served to auser based on at least one of: the category (or label) or content of acaptured image, and/or the category of content of the promotion orrecommendation. Indeed, according to some embodiments, the content thatforms the basis of the advertisement(s) can be based on a label of animage, a category/label of an image, the features and descriptorsassociated with a stored image or captured image, or other contextualdata surrounding the image or its capture (e.g. the location whereand/or time when the picture was taken, and/or the user taking and/oruploading such picture).

By way of a non-limiting example, work flow 700 enables a userrequesting an image(s) of the Empire State Building (e.g., a search forsuch images as discussed above in relation to FIG. 5) on his mobilephone, whereby an advertisement associated with tickets for Empire StateBuilding's observation deck can be displayed in association with theimage search results comprising at least one new high quality image. Inanother non-limiting embodiments, if a user is determined to be takingpictures with his/her mobile phone of the Grand Canyon, uponidentification of the best image (from the “burst”/series of images orimage data being in focus, as per the discussion in FIGS. 6A-6B), anadvertisement related to admission to the Grand Canyon, or hikingexcursions or hotel deals can be provided to the user. Such ads may beprovided to the user as a push message that is to appear within theapplication the user is using to access the camera on the device, or maybe provided as part of the display when the user loads the image tohis/her image library (e.g., on the user's Flickr® page).

In Step 702, an image is identified. This image can be based on theidentification process outlined above with respect to FIGS. 5-6B. Insome embodiments, the identity of the image can be based on a runtimerequest, such as, but not limited to, a search for an image, or sharingan image, and the like. For purposes of this disclosure, Process 700will refer to a single image being utilized as the basis for serving anadvertisement(s); however, it should not be construed as limiting, asany number of images can form such basis, as well as any type of media,multi-media or hypermedia can for such basis without departing from thescope of the instant disclosure.

In Step 704, the image is analyzed to identify the raw data associatedwith the image. Such analysis is performed in a similar manner asdiscussed above in relation to Step 408 of FIG. 4A, inter alia. Forexample, such analysis can involve parsing the image identified in Step702 and extracting the raw image data. In some embodiments, suchanalysis involves applying a deep learning algorithm (or any other knownor to be known data extraction algorithm) to the identified image todetermine the raw image data of the image (i.e., the features anddescriptors), as discussed above.

In Step 706, a context is determined based on the extracted raw imagedata. As discussed above, in relation to Step 412 and 504, the raw imagedata can provide an indication as to the type of content associated withthe image. For example, a context is determined based on the content ofan image captured and/or promoted, content of an image provided in aruntime request (e.g., search result, shared post, message, and thelike), and/or descriptors, features or labels of the images or othermetadata in the database and/or associated with a captured image. Thiscontext forms a basis for serving advertisements having a similarcontext (i.e., relating to the type of content). In some embodiments,the context can be determined in a similar manner as discussed above inrelation to determining a category of content (as in Step 412, interalia). Thus, Step 706 can involve determining a content type associatedwith the provided image(s), a content category associated with theimage(s), and the like.

In some embodiments, the identification of the context from Step 706 mayoccur during analysis of the image(s) (as discussed above in relation toFIGS. 5-6B), after analysis of the image(s), and/or after or beforecommunication (e.g., recommendation or display) of the image(s) to theuser, or some combination thereof. In Step 708, the context (e.g.,content/context data) is communicated (or shared) with an advertisementplatform comprising an advertisement server 130 and ad database. Uponreceipt of the context, the advertisement server 130 performs a searchfor a relevant advertisement within the associated ad database. Thesearch for an advertisement is based at least on the identified context.

As discussed above, Step 706 can, in addition to determining a contextfrom an image(s), involve the context being further and/or alternativelybased upon content associated with, derived or extracted from animage(s) and/or other types of content items/objects or types. Forpurposes of work flow 700 reference will be made to an image(s) forsimplicity; however, it should not be viewed as limiting the scope ofthe instant disclosure and applicability of work flow 700 to theembodiments discussed herein.

In Step 708, the advertisement server 130 searches the ad database foradvertisements that match the identified context. In Step 710, anadvertisement is selected (or retrieved) based on the results of Step708. In some embodiments, the advertisement can be selected based uponthe result of Step 708, and modified to conform to attributes of thepage, message or method upon which the advertisement will be displayed,and/or to the application and/or device for which it will be displayed.In some embodiments, as in Step 712, the selected advertisement isshared or communicated via the application the user is utilizing torequest, capture and/or access the image(s). In some embodiments, theselected advertisement is sent directly to each user's computing device.In some embodiments, the selected advertisement is displayed inconjunction with the requested, captured and/or accessed image(s) on theuser's device and/or within the application being used to request,capture and/or access the image(s).

As shown in FIG. 8, internal architecture 800 of a computing device(s),computing system, computing platform and the like includes one or moreprocessing units, processors, or processing cores, (also referred toherein as CPUs) 812, which interface with at least one computer bus 802.Also interfacing with computer bus 802 are computer-readable medium, ormedia, 806, network interface 814, memory 804, e.g., random accessmemory (RAM), run-time transient memory, read only memory (ROM), mediadisk drive interface 820 as an interface for a drive that can readand/or write to media including removable media such as floppy, CD-ROM,DVD, media, display interface 810 as interface for a monitor or otherdisplay device, keyboard interface 816 as interface for a keyboard,pointing device interface 818 as an interface for a mouse or otherpointing device, and miscellaneous other interfaces not shownindividually, such as parallel and serial port interfaces and auniversal serial bus (USB) interface.

Memory 804 interfaces with computer bus 802 so as to provide informationstored in memory 804 to CPU 812 during execution of software programssuch as an operating system, application programs, device drivers, andsoftware modules that comprise program code, and/or computer executableprocess steps, incorporating functionality described herein, e.g., oneor more of process flows described herein. CPU 812 first loads computerexecutable process steps from storage, e.g., memory 804, computerreadable storage medium/media 806, removable media drive, and/or otherstorage device. CPU 812 can then execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 812during the execution of computer-executable process steps.

Persistent storage, e.g., medium/media 806, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

Network link 828 typically provides information communication usingtransmission media through one or more networks to other devices thatuse or process the information. For example, network link 828 mayprovide a connection through local network 824 to a host computer 826 orto equipment operated by a Network or Internet Service Provider (ISP)830. ISP equipment in turn provides data communication services throughthe public, worldwide packet-switching communication network of networksnow commonly referred to as the Internet 832.

A computer called a server host 834 connected to the Internet 832 hostsa process that provides a service in response to information receivedover the Internet 832. For example, server host 834 hosts a process thatprovides information representing video data for presentation at display810. It is contemplated that the components of system 800 can bedeployed in various configurations within other computer systems, e.g.,host and server.

At least some embodiments of the present disclosure are related to theuse of computer system 800 for implementing some or all of thetechniques described herein. According to one embodiment, thosetechniques are performed by computer system 800 in response toprocessing unit 812 executing one or more sequences of one or moreprocessor instructions contained in memory 804. Such instructions, alsocalled computer instructions, software and program code, may be readinto memory 804 from another computer-readable medium 806 such asstorage device or network link. Execution of the sequences ofinstructions contained in memory 804 causes processing unit 812 toperform one or more of the method steps described herein. In alternativeembodiments, hardware, such as ASIC, may be used in place of or incombination with software. Thus, embodiments of the present disclosureare not limited to any specific combination of hardware and software,unless otherwise explicitly stated herein.

The signals transmitted over network link and other networks throughcommunications interface, carry information to and from computer system800. Computer system 800 can send and receive information, includingprogram code, through the networks, among others, through network linkand communications interface. In an example using the Internet, a serverhost transmits program code for a particular application, requested by amessage sent from computer, through Internet, ISP equipment, localnetwork and communications interface. The received code may be executedby processor 802 as it is received, or may be stored in memory 804 or instorage device or other non-volatile storage for later execution, orboth.

For the purposes of this disclosure a module is a software, hardware, orfirmware (or combinations thereof) system, process or functionality, orcomponent thereof, that performs or facilitates the processes, features,and/or functions described herein (with or without human interaction oraugmentation). A module can include sub-modules. Software components ofa module may be stored on a computer readable medium for execution by aprocessor. Modules may be integral to one or more servers, or be loadedand executed by one or more servers. One or more modules may be groupedinto an engine or an application.

For the purposes of this disclosure the term “user”, “subscriber”“consumer” or “customer” should be understood to refer to a user of anapplication or applications as described herein and/or a consumer ofdata supplied by a data provider. By way of example, and not limitation,the term “user” or “subscriber” can refer to a person who receives dataprovided by the data or service provider over the Internet in a browsersession, or can refer to an automated software application whichreceives the data and stores or processes the data.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client level or server level or both. In thisregard, any number of the features of the different embodimentsdescribed herein may be combined into single or multiple embodiments,and alternate embodiments having fewer than, or more than, all of thefeatures described herein are possible.

Functionality may also be, in whole or in part, distributed amongmultiple components, in manners now known or to become known. Thus,myriad software/hardware/firmware combinations are possible in achievingthe functions, features, interfaces and preferences described herein.Moreover, the scope of the present disclosure covers conventionallyknown manners for carrying out the described features and functions andinterfaces, as well as those variations and modifications that may bemade to the hardware or software or firmware components described hereinas would be understood by those skilled in the art now and hereafter.

Furthermore, the embodiments of methods presented and described asflowcharts in this disclosure are provided by way of example in order toprovide a more complete understanding of the technology. The disclosedmethods are not limited to the operations and logical flow presentedherein. Alternative embodiments are contemplated in which the order ofthe various operations is altered and in which sub-operations describedas being part of a larger operation are performed independently.

While various embodiments have been described for purposes of thisdisclosure, such embodiments should not be deemed to limit the teachingof this disclosure to those embodiments. Various changes andmodifications may be made to the elements and operations described aboveto obtain a result that remains within the scope of the systems andprocesses described in this disclosure.

1-10. (canceled)
 11. A method comprising: capturing, via a computingdevice, a set of images, said captured images comprising raw image datagenerated from said capturing; parsing, via the computing device, theset of images, and based on said parsing, extracting, via the computingdevice, the raw image data, said raw image data of each image comprisingfeatures associated with content of each captured image; comparing, viathe computing device, the raw image data of each image with raw imagedata of a second image, said comparison comprising identifying asimilarity between the raw image data of the images and the raw imagedata of the second image; determining, via the computing device, aquality of each image based on said comparison, said quality based onthe identified similarity and comprising a value indicating saididentified similarity; identifying, via the computing device, an imagefrom the set of images having a highest quality based said determinedquality value of each image; and communicating, via the computingdevice, information associated with the identified image to a user ofthe computing device.
 12. The method of claim 11, further comprising:determining the identified image to be a high quality image, said highquality determination based on the similarity of the raw image data ofthe identified image satisfying a comparison threshold.
 13. The methodof claim 11, further comprising: ranking each image in the set of imagesbased on the identified similarity, wherein said ranking comprises saididentified image being atop the ranking and lower ranked images having adecreasing similarity value.
 14. The method of claim 11, wherein saidcomparison further comprises: translating the features of each image inthe image set into a feature vector for each image, wherein each featurevector has a dimensional value proportional to a number of features;extracting features of the second image based on the second raw imagedata; translating the features of the second image into a feature vectorhaving a dimensional value proportional to a number of features of thesecond image; and determining said similarity based on a semanticsimilarity between each data point of each feature vector of each imagein the image set and each data point of the second feature vector. 15.The method of claim 11, further comprising: determining a contentcategory of the image set based on the raw image data of the image set,wherein said second image corresponds to the determined contentcategory.
 16. The method of claim 11, wherein said second image is ahigh-quality image, wherein a level of interest on a network is at orabove a quality threshold associated with a requisite level of userinterest. 17-20. (canceled)
 21. The method of claim 11, furthercomprising: determining a context of the identified image based on theraw image data; causing communication, over the network, of said contextto an advertisement platform to obtain a digital content objectcomprising digital advertisement content associated with said context;and communicating said identified digital content object in associationwith said communication of said identified image.
 22. A non-transitorycomputer-readable storage medium tangibly encoded withcomputer-executable instructions, that when executed by a processorassociated with a computing device, performs a method comprising:capturing, via a computing device, a set of images, said captured imagescomprising raw image data generated from said capturing; parsing, viathe computing device, the set of images, and based on said parsing,extracting, via the computing device, the raw image data, said raw imagedata of each image comprising features associated with content of eachcaptured image; comparing, via the computing device, the raw image dataof each image with raw image data of a second image, said comparisoncomprising identifying a similarity between the raw image data of theimages and the raw image data of the second image; determining, via thecomputing device, a quality of each image based on said comparison, saidquality based on the identified similarity and comprising a valueindicating said identified similarity; identifying, via the computingdevice, an image from the set of images having a highest quality basedsaid determined quality value of each image; and communicating, via thecomputing device, information associated with the identified image to auser of the computing device.
 23. The non-transitory computer-readablestorage medium of claim 22, further comprising: determining theidentified image to be a high quality image, said high qualitydetermination based on the similarity of the raw image data of theidentified image satisfying a comparison threshold.
 24. Thenon-transitory computer-readable storage medium of claim 22, furthercomprising: ranking each image in the set of images based on theidentified similarity, wherein said ranking comprises said identifiedimage being atop the ranking and lower ranked images having a decreasingsimilarity value.
 25. The non-transitory computer-readable storagemedium of claim 22, wherein said comparison further comprises:translating the features of each image in the image set into a featurevector for each image, wherein each feature vector has a dimensionalvalue proportional to a number of features; extracting features of thesecond image based on the second raw image data; translating thefeatures of the second image into a feature vector having a dimensionalvalue proportional to a number of features of the second image; anddetermining said similarity based on a semantic similarity between eachdata point of each feature vector of each image in the image set andeach data point of the second feature vector.
 26. The non-transitorycomputer-readable storage medium of claim 22, further comprising:determining a content category of the image set based on the raw imagedata of the image set, wherein said second image corresponds to thedetermined content category.
 27. The non-transitory computer-readablestorage medium of claim 22, wherein said second image is a high-qualityimage, wherein a level of interest on a network is at or above a qualitythreshold associated with a requisite level of user interest.
 28. Acomputing device comprising: a processor; a non-transitorycomputer-readable storage medium for tangibly storing thereon programlogic for execution by the processor, the program logic comprising:logic executed by the processor for capturing, via the computing device,a set of images, said captured images comprising raw image datagenerated from said capturing; logic executed by the processor forparsing, via the computing device, the set of images, and based on saidparsing, extracting, via the computing device, the raw image data, saidraw image data of each image comprising features associated with contentof each captured image; logic executed by the processor for comparing,via the computing device, the raw image data of each image with rawimage data of a second image, said comparison comprising identifying asimilarity between the raw image data of the images and the raw imagedata of the second image; logic executed by the processor fordetermining, via the computing device, a quality of each image based onsaid comparison, said quality based on the identified similarity andcomprising a value indicating said identified similarity; logic executedby the processor for identifying, via the computing device, an imagefrom the set of images having a highest quality based said determinedquality value of each image; and logic executed by the processor forcommunicating, via the computing device, information associated with theidentified image to a user of the computing device.
 29. The computingdevice of claim 28, further comprising: logic executed by the processorfor determining the identified image to be a high quality image, saidhigh quality determination based on the similarity of the raw image dataof the identified image satisfying a comparison threshold.
 30. Thecomputing device of claim 28, further comprising: logic executed by theprocessor for ranking each image in the set of images based on theidentified similarity, wherein said ranking comprises said identifiedimage being atop the ranking and lower ranked images having a decreasingsimilarity value.
 31. The computing device of claim 28, wherein saidcomparison further comprises: logic executed by the processor fortranslating the features of each image in the image set into a featurevector for each image, wherein each feature vector has a dimensionalvalue proportional to a number of features; logic executed by theprocessor for extracting features of the second image based on thesecond raw image data; logic executed by the processor for translatingthe features of the second image into a feature vector having adimensional value proportional to a number of features of the secondimage; and logic executed by the processor for determining saidsimilarity based on a semantic similarity between each data point ofeach feature vector of each image in the image set and each data pointof the second feature vector.
 32. The computing device of claim 28,further comprising: logic executed by the processor for determining acontent category of the image set based on the raw image data of theimage set, wherein said second image corresponds to the determinedcontent category.
 33. The computing device of claim 28, wherein saidsecond image is a high-quality image, wherein a level of interest on anetwork is at or above a quality threshold associated with a requisitelevel of user interest.